Covid19Japan.com
Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。
Import
Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。
個票データ（Patient Data）
陽性者単位の個票データ。
path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"
df <- path %>%
paste0("latest.json") %>%
readr::read_lines() %>%
paste0(path, .) %>%
jsonlite::fromJSON()
df
集計データ（Summary Data）
死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。
path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"
df_s <- path %>%
paste0("latest.json") %>%
readr::read_lines() %>%
paste0(path, .) %>%
jsonlite::fromJSON()
df_s %>% summary()## Length Class Mode
## prefectures 27 data.frame list
## regions 12 data.frame list
## daily 37 data.frame list
## updated 1 -none- character
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。
都道府県単位集計
更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。
df_s$prefectures陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。
| 項目 | 内容 | 備考 |
|---|---|---|
| dailyConfirmedCount | 陽性者数 | 単日 |
| dailyConfirmedStartDate | 陽性者数のカウント開始日 | 区分により開始日が異なる |
| dailyDeceasedCount | 死亡者数 | 単日 |
| dailyDeceasedStartDate | 死亡者数のカウント開始日 | 区分により開始日が異なる |
| dailyRecoveredCumulative | 快復者数 | 累計 |
| dailyRecoveredStartDate | 快復者数のカウント開始日 | 区分により開始日が異なる |
| dailyActive | 治療者数1 | 単日 |
| dailyActiveStartDate | 治療者数のカウント開始日 | 区分により開始日が異なる |
1 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている
地方単位集計
更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。
df_s$regionsdf_s$regions$confirmed[1]## [1] 71003df_s$regions$dailyConfirmedCount[[1]] %>% sum()## [1] 77868
日次集計
個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。
df_s$daily
更新日時
集計データの更新日時。
df_s$updated## [1] "2020-11-28T21:37:32+09:00"
Others
病床データ
新型コロナウイルス対策病床オープンデータのデータも用意しておく。
if (googlesheets4::gs4_has_token()) {
beds_by_pref <- "https://docs.google.com/spreadsheets/d/1u0Ul8TgJDqoZMnqFrILyXzTHvuHMht1El7wDZeVrpp8" %>%
googlesheets4::read_sheet() %>%
dplyr::arrange(dplyr::desc(`発表日`)) %>%
dplyr::distinct(`自治体名`, .keep_all = TRUE) %>%
dplyr::rename(pref = `自治体名`, beds = `新型コロナウイルス対策感染症病床数`,
date = `発表日`) %>%
dplyr::mutate(beds = as.integer(beds), date = lubridate::as_date(date))
beds_by_pref
}
対策ダッシュボード
NECソリューションイノベータによる都道府県単位の単日集計データ。治療に関する集計データが含まれている。ただし、項目によっては累積値（累計値）のものもある。
"https://covid-19.nec-solutioninnovators.com/download/japan_covid19.csv" %>%
readr::read_csv(guess_max = 12500) %>%
dplyr::select(date = `公表_年月日`, pref = `都道府県名`,
confirmed = `PCR検査陽性者`, pcr = `PCR検査実施人数`,
`入院治療等を要する者`, `うち重症`, `退院又は療養解除となった者の数`,
`確認中`) %>%
dplyr::mutate(date = lubridate::as_date(date)) %>%
dplyr::mutate_if(is.numeric, .funs = as.integer)
新型コロナ関連ニュース
新型コロナ関連のニュース
news <- "https://gist.githubusercontent.com/k-metrics/76fea197fa32466a2f99ff59f721b98a/raw/4c971a6cde2033e458525b793e832c4a87cbae2d/covid19_news.csv" %>%
readr::read_csv() %>%
dplyr::filter(area == "日本")
news
Summarize
最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。
df %>%
skimr::skim()| Name | Piped data |
| Number of rows | 147370 |
| Number of columns | 23 |
| _______________________ | |
| Column type frequency: | |
| character | 19 |
| logical | 3 |
| numeric | 1 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 1 | 8 | 0 | 145441 | 0 |
| dateAnnounced | 0 | 1.00 | 10 | 10 | 0 | 305 | 0 |
| gender | 42130 | 0.71 | 1 | 1 | 0 | 2 | 0 |
| detectedPrefecture | 0 | 1.00 | 3 | 15 | 0 | 49 | 0 |
| patientStatus | 143159 | 0.03 | 8 | 23 | 0 | 8 | 0 |
| notes | 79791 | 0.46 | 1 | 270 | 0 | 64635 | 1 |
| mhlwPatientNumber | 146921 | 0.00 | 1 | 11 | 0 | 434 | 0 |
| prefecturePatientNumber | 38051 | 0.74 | 5 | 20 | 0 | 109310 | 0 |
| prefectureSourceURL | 115978 | 0.21 | 5 | 224 | 0 | 3454 | 0 |
| residence | 50459 | 0.66 | 1 | 38 | 0 | 1429 | 0 |
| sourceURL | 1183 | 0.99 | 1 | 239 | 0 | 8911 | 0 |
| relatedPatients | 135474 | 0.08 | 2 | 259 | 0 | 7096 | 0 |
| knownCluster | 144836 | 0.02 | 3 | 88 | 0 | 235 | 0 |
| detectedCityTown | 119194 | 0.19 | 2 | 22 | 0 | 667 | 0 |
| cityPrefectureNumber | 119495 | 0.19 | 1 | 34 | 0 | 27866 | 2 |
| citySourceURL | 135219 | 0.08 | 9 | 317 | 0 | 3688 | 0 |
| deceasedDate | 145318 | 0.01 | 10 | 10 | 0 | 255 | 0 |
| deceasedReportedDate | 146146 | 0.01 | 10 | 62 | 0 | 208 | 0 |
| deathSourceURL | 146291 | 0.01 | 14 | 123 | 0 | 658 | 0 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 0.99 | TRU: 145440, FAL: 1930 |
| charterFlightPassenger | 147356 | 0 | 1.00 | TRU: 14 |
| cruisePassengerDisembarked | 147359 | 0 | 1.00 | TRU: 11 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ageBracket | 0 | 1 | 26.83 | 24.85 | -1 | -1 | 20 | 40 | 100 | ▇▇▅▂▁ |
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。
Tidy & Transform
各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。なお、都道府県以外で報告されたレコードを除いている。
x <- df %>%
dplyr::select(patientId, date = dateAnnounced, gender,
pref = detectedPrefecture, patientStatus, knownCluster,
confirmedPatient, charterFlightPassenger,
cruisePassengerDisembarked, ageBracket,
deceasedDate, deceasedReportedDate) %>%
dplyr::filter(confirmedPatient == TRUE) %>%
dplyr::mutate(date = lubridate::as_date(date),
gender = forcats::as_factor(gender),
patientStatus = forcats::as_factor(patientStatus),
cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
ageBracket = forcats::as_factor(ageBracket),
deceasedDate = lubridate::as_date(deceasedDate),
deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::select(-`推計人口`, -pref) %>%
dplyr::rename(pref = `都道府県`, region = `八地方区分`) %>%
tidyr::drop_na(pref)
x変換結果を要約してみると
x %>%
skimr::skim()| Name | Piped data |
| Number of rows | 143926 |
| Number of columns | 18 |
| _______________________ | |
| Column type frequency: | |
| character | 2 |
| Date | 3 |
| factor | 9 |
| logical | 4 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| patientId | 0 | 1.00 | 2 | 8 | 0 | 143926 | 0 |
| knownCluster | 141442 | 0.02 | 3 | 88 | 0 | 232 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| date | 0 | 1 | 2020-01-15 | 2020-11-28 | 2020-09-09 | 302 |
| deceasedDate | 143547 | 0 | 2020-02-13 | 2020-11-19 | 2020-05-08 | 150 |
| deceasedReportedDate | 143597 | 0 | 2020-02-13 | 2020-10-17 | 2020-05-16 | 130 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| gender | 40283 | 0.72 | FALSE | 2 | M: 58020, F: 45623 |
| patientStatus | 141415 | 0.02 | FALSE | 8 | Hos: 1246, Dec: 371, Hom: 315, Dis: 276 |
| ageBracket | 0 | 1.00 | FALSE | 13 | -1: 40368, 20: 27962, 30: 17982, 40: 15068 |
| pcode | 0 | 1.00 | FALSE | 47 | 13: 40259, 27: 19631, 14: 12347, 23: 9861 |
| pref | 0 | 1.00 | FALSE | 47 | 東京都: 40259, 大阪府: 19631, 神奈川: 12347, 愛知県: 9861 |
| region | 0 | 1.00 | FALSE | 8 | 関東地: 71003, 近畿地: 30873, 中部地: 15430, 九州地: 12967 |
| 広域圏 | 12768 | 0.91 | FALSE | 8 | 首都圏: 71341, 近畿圏: 30048, 中部圏: 13970, 九州圏: 8714 |
| 通俗的区分 | 0 | 1.00 | FALSE | 11 | 関東: 71003, 関西: 30048, 東海: 13280, 九州: 8714 |
| fct_pref | 0 | 1.00 | FALSE | 47 | Tok: 40259, Osa: 19631, Kan: 12347, Aic: 9861 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| confirmedPatient | 0 | 1 | 1.00 | TRU: 143926 |
| charterFlightPassenger | 143919 | 0 | 1.00 | TRU: 7 |
| cruisePassengerDisembarked | 143915 | 0 | 1.00 | TRU: 11 |
| cluster | 0 | 1 | 0.02 | FAL: 141442, TRU: 2484 |
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば
- 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
- 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
- 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い
ことが読める。
patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合は集計データを使った方がいいことが分かる。
x %>%
dplyr::group_by(patientStatus) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
"重症", "自宅療養", "ホテル療養", NA))
Data Wrangling
陽性者の集計
最初に陽性者をキーに集計する。
全国集計
全国の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
r_by_all <- x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::summarise(n = n()) %>%
dplyr::bind_cols(prefs %>% dplyr::summarise(population = sum(`推計人口`))) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_all %>%
dplyr::rename(`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
地方別集計
次に地方別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。
region <- prefs %>%
dplyr::group_by(`八地方区分`) %>%
dplyr::summarise(population = sum(`推計人口`)) %>%
dplyr::rename(region = `八地方区分`)
r_by_region <- x %>%
dplyr::group_by(region) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(region, by = c("region" = "region")) %>%
dplyr::select(region, n, population) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_region %>%
dplyr::rename(`地方` = region,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate)
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.14）。
r_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
都道府県別集計
同様に都道府県別の累計陽性者数と推計人口[千人]、ならびに、人口千人あたりの累計陽性者数。任意の列でソートできるようにしてある。
r_by_pref <- x %>%
dplyr::group_by(pref) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
dplyr::select(pref, n, population = `推計人口`) %>%
dplyr::mutate(rate = round(n / population, 2))
r_by_pref %>%
dplyr::rename(`都道府県` = pref,
`累計陽性者数[人]` = n, `推計人口[千人]` = population,
`人口千人あたりの累計陽性者数` = rate) %>%
tibble::rowid_to_column("No") %>%
DT::datatable()
上表を可視化する。グレーの破線は切片ゼロで傾きが全国の人口千人あたりの累計陽性者数（1.14）。
r_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
推計人口が550万人未満の都道府県のみ抽出する。グレーの破線は上図と同様。
r_by_pref %>%
dplyr::filter(population < 5500) %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = r_by_all$rate, intercept = 0,
colour = "gray", linetype = "dashed") +
ggplot2::geom_point(ggplot2::aes(colour = key)) +
ggrepel::geom_text_repel(ggplot2::aes(label = key, colour = key)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数[人]")
クラスタ比率
全国のクラスタ比率
x %>%
dplyr::filter(!is.na(pref)) %>%
dplyr::group_by(cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)地方別クラスタ比率
地方別の累計陽性者数の内、クラスタ感染と判定された人数の割合を求める。
x %>%
dplyr::group_by(region, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
dplyr::rename(`地方` = region,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio)
都道府県別クラスタ比率
同様に都道府県別のクラスタ比率。任意の列でソートできるようにしてある。
x %>%
dplyr::group_by(pref, cluster) %>%
dplyr::summarise(n = n()) %>%
tidyr::drop_na() %>%
tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>%
tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>%
dplyr::rename(`都道府県` = pref,
`非クラスタ感染者[人]` = `FALSE`, `クラスタ感染者[人]` = `TRUE`,
`クラスタ比率[%]` = ratio) %>%
tibble::rowid_to_column(var = "No") %>%
DT::datatable()
陽性者の日次集計
全国日次集計
全国の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_all <- x %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = n()) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
fill = list(n = 0L)) %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n), ma28 = ma28(n))
x_by_all %>%
dplyr::select(`発表日` = date, `陽性者数` = n, `前日差` = diff,
`累計陽性者数` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
# 祝日ファイルは以下をダウンロードしておく（SSLがエラーになるので自動処理できない）
# "https://www8.cao.go.jp/chosei/shukujitsu/syukujitsu.csv"
# 祝日判定関数
source("https://raw.githubusercontent.com/logics-of-blue/website/master/010_forecast/20190714_R%E8%A8%80%E8%AA%9E%E3%81%AB%E3%81%8A%E3%81%91%E3%82%8B%E6%97%A5%E6%9C%AC%E3%81%AE%E7%A5%9D%E6%97%A5%E5%88%A4%E5%AE%9A/jholiday.R", encoding="utf-8")
sec_scale <- 100
emergency <- news %>%
dplyr::filter(category == "緊急事態")
goto <- news %>%
dplyr::filter(category == "GoTo")
x_by_all %>%
dplyr::mutate(hday = is.jholiday(target_date = date,
holiday_source = "./Covid19/syukujitsu.csv"),
wday = lubridate::wday(date, week_start = 1),
wday = dplyr::if_else(wday > 5, TRUE, FALSE),
wday = dplyr::if_else(hday | wday, 3000L, NA_integer_)) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = wday), stat = "identity", width = 1.0,
fill = "red", alpha = 0.1) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = emergency,
colour = "dark blue", linetype = "dashed", size = 0.15) +
ggplot2::geom_vline(ggplot2::aes(xintercept = date), data = goto,
colour = "magenta", linetype = "dashed", size = 0.25) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
fill = "dark gray", alpha = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("【全国】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2500, label = news),
size = 2.0, data = emergency) +
ggrepel::geom_label_repel(ggplot2::aes(x = date, y = 2250, label = news),
size = 2.5, data = goto, colour = "magenta") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("【全国】陽性者数の前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別日次集計
同様に地方別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_region <- x %>%
dplyr::group_by(date, region) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
by = c("八地方区分" = "region")) %>%
dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>%
dplyr::arrange(date)
x_by_region %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)x_by_region %>%
dplyr::select(`地方` = region,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = n)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
width = 1.0, alpha = 0.5) +
ggplot2::labs(title = paste0("【地方別】陽性者数の推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") 
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】移動平均（7日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) +
ggplot2::geom_line(size = 1) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("【地方別】累計陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "累計陽性者数") +
ggrepel::geom_text_repel(ggplot2::aes(label = region),
data = subset(x_by_region, date == max(date)),
nudge_x = 30, segment.alpha = 0.5, size = 4) +
ggplot2::lims(x = c(min(x_by_region$date),
max(x_by_region$date) + 45))
地方単位で可視化。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dotted", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（点線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
傾向が見えるように縦軸をフリースケールとする。
sec_scale <- 20
ncol <- 2
x_by_region %>%
dplyr::rename(key = region) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.5, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "dashed", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "陽性者累計（実線）")
)
都道府県別日次集計
同様に都道府県別の日次単位の陽性者数、前日差、累計、移動平均を求める。
x_by_pref <- x %>%
dplyr::group_by(date, pref) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = pref, values_from = n, values_fill = 0L) %>%
tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>%
tidyr::pivot_longer(cols = -date, names_to = "pref", values_to = "n") %>%
tidyr::replace_na(replace = list(n = 0L)) %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n)),
ma28 = purrr::map(data, ~ ma28(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::arrange(date)
x_by_pref %>%
dplyr::filter(date == max(date)) %>%
dplyr::mutate(ma7 = round(ma7, 1)) %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7) %>%
DT::datatable()x_by_pref %>%
dplyr::select(`都道府県` = pref,
`発表日` = date, `陽性者数` = n, `前日差` = diff,
`陽性者累計` = cum, `移動平均（7日）` = ma7)
上表を可視化する。
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
傾向が見えるように縦軸をフリースケールとする。
x_by_pref %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
死亡者の日次集計
厚生労働省のデータと乖離がある。
都道府県別
都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。
start <- df_s$prefectures %>%
dplyr::select(pref = name, date = dailyDeceasedStartDate) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
dplyr::arrange(pcode) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(date, pref = `都道府県`) %>%
dplyr::distinct(date) %>%
.$date %>% lubridate::as_date()
d_by_prefs <- df_s$prefectures %>%
dplyr::select(deceased = dailyDeceasedCount, pref = name) %>%
dplyr::left_join(prefs, by = c("pref" = "pref")) %>%
tidyr::drop_na(pcode) %>%
dplyr::select(pref = `都道府県`, deceased) %>%
tidyr::unnest(deceased) %>%
tidyr::pivot_wider(names_from = pref, values_from = deceased) %>%
tidyr::unnest() %>%
dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
by = "day")) %>%
dplyr::select(date, dplyr::everything()) %>%
tidyr::pivot_longer(col = -date, names_to = "pref", values_to = "n") %>%
dplyr::group_by(pref) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::left_join(prefs, ., by = c("都道府県" = "pref")) %>%
dplyr::mutate(pref = forcats::fct_inorder(`都道府県`)) %>%
dplyr::select(date, pref, n, diff, cum, ma7) %>%
dplyr::arrange(date)
d_by_prefssec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
dplyr::rename(key = pref) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = key), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = key),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = key)) +
ggplot2::facet_wrap(~ key, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計陽性者数（折線）")
)
地方別
集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。
d_by_region <- d_by_prefs %>%
dplyr::select(date, pref = pref, n) %>%
dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>%
tidyr::drop_na(pcode) %>%
dplyr::group_by(date, `八地方区分`) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::rename(region = `八地方区分`) %>%
dplyr::group_by(region) %>%
tidyr::nest() %>%
dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
cum = purrr::map(data, ~ cumsum(.$n)),
ma7 = purrr::map(data, ~ ma7(.$n))) %>%
tidyr::unnest() %>%
dplyr::arrange(date)
d_by_region
陽性者比率と死亡者比率
rpd_by_all <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population) %>%
dplyr::select(-region) %>%
dplyr::summarise_all(sum) %>%
dplyr::mutate(p_rate = round(positive / population, 2),
d_rate = round(deceased / positive, 2))
rpd_by_all %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_region <- d_by_region %>%
dplyr::group_by(region) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_region, ., by = c("region")) %>%
dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_region %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
rpd_by_prefs <- d_by_prefs %>%
dplyr::group_by(pref) %>%
dplyr::summarise(d = sum(n)) %>%
dplyr::left_join(r_by_pref, ., by = "pref") %>%
dplyr::select(pref, positive = n, deceased = d, population, p_rate = rate) %>%
dplyr::mutate(d_rate = round(deceased / positive, 2))
rpd_by_prefs %>%
dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
`推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
`陽性者に対する死亡者比率` = d_rate)
全国日次集計
都道府県別のデータから全国の日次集計を求める。
d_by_all <- d_by_prefs %>%
dplyr::group_by(date) %>%
dplyr::summarise(n = sum(n)) %>%
dplyr::ungroup() %>%
dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_allsec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), fill = "dark gray", stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7), colour = "dark green",
linetype = "dashed", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale), colour = "dark green") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・同移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累計死亡者数（実線）")
)
年代別
x_by_age <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9"))
x_by_agex %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(rate = round((n / sum(n) * 100), 1))
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
tidyr::pivot_wider(names_from = ageBracket, values_from = n)x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(region, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(region = forcats::fct_rev(region)) %>%
ggplot2::ggplot(ggplot2::aes(x = region)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::facet_grid(~ cluster) +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
都道府県別
g_age_by_pref <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
# dplyr::group_by(pref, ageBracket) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
# ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref
g_age_by_pref +
ggplot2::facet_grid(~ cluster)
g_age_by_pref_wo <- x %>%
dplyr::mutate(ageBracket = forcats::fct_recode(ageBracket, Unknown = "-1"),
ageBracket = forcats::fct_collapse(ageBracket, `0-9` = c("0", "5"))) %>%
dplyr::mutate(ageBracket = forcats::fct_relevel(ageBracket,
"100", "90", "80", "70", "60",
"50", "40", "30", "20", "10",
"0-9")) %>%
dplyr::filter(ageBracket != "Unknown") %>%
dplyr::group_by(pref, ageBracket, cluster) %>%
dplyr::summarise(n = n()) %>%
dplyr::mutate(pref = forcats::fct_rev(pref)) %>%
ggplot2::ggplot(ggplot2::aes(x = pref)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = ageBracket), stat = "identity",
position = "fill") +
ggplot2::scale_fill_brewer(palette = "Set3") +
ggplot2::scale_y_continuous(labels = scales::percent) +
ggplot2::coord_flip() +
ggplot2::geom_hline(yintercept = c(0.25, 0.5, 0.75), size = 0.35,
colour = "dark gray") +
ggplot2::labs(title = paste0("年齢不明者をのぞく年代構成比 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "")
g_age_by_pref_wo
g_age_by_pref_wo +
ggplot2::facet_grid(~ cluster) +
ggplot2::labs(title = paste0("年齢不明者をのぞく非クラスタ/クラスタでの年代構成比 @", datetime))
Visualize
前日差
x_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
ggplot2::facet_wrap(~ region, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
caption = caption, x = "", y = "")
都道府県別
単日＋累計
sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積陽性者数（折線）")
)
前日差
x_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
x = "", y = "")
死亡者の日次推移
全国
sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
alpha = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
colour = "dark green", size = 0.5) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
colour = "dark green", size = 1.0) +
ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（破線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_all %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) +
ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "前日差")
地方別
sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
linetype = "solid", size = 0.2) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime),
subtitle = subtitle, caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数・移動平均（細線）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
都道府県別日次推移
sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.25, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
d_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n, fill = pref), stat = "identity",
alpha = 0.35, width = 1.0) +
ggplot2::geom_line(ggplot2::aes(y = ma7, colour = pref),
linetype = "solid", size = 0.25) +
ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = pref)) +
ggplot2::facet_wrap(~ pref, ncol = ncol, scales = "free_y") +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "死亡者数（単日）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "累積死亡者数（折線）")
)
比較
陽性者数と死亡者の比較。
全国
sec_scale <- (1 / 50)
x_by_all %>%
dplyr::left_join(d_by_all, by = c("date")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
地方別
sec_scale <- (1 / 10)
ncol <- 4
x_by_region %>%
dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>%
ggplot2::ggplot(ggplot2::aes(x = date)) +
ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
fill = "dark green", alpha = 0.25, width = 1.0) +
ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
fill = "dark red", alpha = 0.25, width = 1.0) +
# ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") +
# ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") +
ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") +
ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
x = "", y = "") +
ggplot2::scale_y_continuous(
name = "陽性者数（濃緑）",
sec.axis = ggplot2::sec_axis(~ . * sec_scale,
name = "死亡者数（濃赤）")
)
相関
地方区分別
r_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_region %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_point(ggplot2::aes(colour = region)) +
ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
都道府県別
r_by_pref %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("@", datetime), caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
r_by_pref %>%
dplyr::filter(n < 5000) %>%
ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) +
ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
caption = caption,
x = "推計人口[千人]", y = "累計陽性者数")
rpd_by_prefs %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
rpd_by_prefs %>%
dplyr::filter(positive < 1000) %>%
ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) +
ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
ggplot2::geom_point(ggplot2::aes(colour = pref)) +
ggrepel::geom_text_repel(ggplot2::aes(label = pref, colour = pref)) +
ggplot2::theme(legend.position = 'none') +
ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
subtitle = subtitle, caption = caption,
x = "陽性者数", y = "死亡者数")
Model
時系列（TS）分析
日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。
ts_week <- x_by_all %>%
dplyr::select(n) %>%
ts(frequency = 7)時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。
ts_week %>%
plot(main = paste0("全国 @", datetime))
上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。
ts_week %>%
base::diff() %>%
plot(main = paste0("全国 @", datetime))
トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。
ts_week %>%
stats::decompose() %>%
plot()
トレンドを抜き出してみる。移動平均に酷似している。
ts_week %>%
stats::decompose() %>%
.$x %>%
plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))
par(new = TRUE)
ts_week %>%
stats::decompose() %>%
.$trend %>%
plot(ylim = c(0, 1500), col = "dark green", lwd = 3)
地方別時系列分析
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLoldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道地方
## NULL
##
## $東北地方
## NULL
##
## $関東地方
## NULL
##
## $中部地方
## NULL
##
## $近畿地方
## NULL
##
## $中国地方
## NULL
##
## $四国地方
## NULL
##
## $九州地方
## NULLpar(oldpar)x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, .y) {
plot(.x, main = .y, ylim = c(0, max(.x)), col = "dark gray")
# plot(.x, main = region)
par(new = TRUE)
stats::decompose(.x) %>%
.$trend %>%
plot(ylim = c(0, max(.x)), col = "dark green", lwd = 2)
} )
## $北海道
## NULL
##
## $青森県
## NULL
##
## $岩手県
## NULL
##
## $宮城県
## NULL
##
## $秋田県
## NULL
##
## $山形県
## NULL
##
## $福島県
## NULL
##
## $茨城県
## NULL
##
## $栃木県
## NULL
##
## $群馬県
## NULL
##
## $埼玉県
## NULL
##
## $千葉県
## NULL
##
## $東京都
## NULL
##
## $神奈川県
## NULL
##
## $新潟県
## NULL
##
## $富山県
## NULL
##
## $石川県
## NULL
##
## $福井県
## NULL
##
## $山梨県
## NULL
##
## $長野県
## NULL
##
## $岐阜県
## NULL
##
## $静岡県
## NULL
##
## $愛知県
## NULL
##
## $三重県
## NULL
##
## $滋賀県
## NULL
##
## $京都府
## NULL
##
## $大阪府
## NULL
##
## $兵庫県
## NULL
##
## $奈良県
## NULL
##
## $和歌山県
## NULL
##
## $鳥取県
## NULL
##
## $島根県
## NULL
##
## $岡山県
## NULL
##
## $広島県
## NULL
##
## $山口県
## NULL
##
## $徳島県
## NULL
##
## $香川県
## NULL
##
## $愛媛県
## NULL
##
## $高知県
## NULL
##
## $福岡県
## NULL
##
## $佐賀県
## NULL
##
## $長崎県
## NULL
##
## $熊本県
## NULL
##
## $大分県
## NULL
##
## $宮崎県
## NULL
##
## $鹿児島県
## NULL
##
## $沖縄県
## NULL
Infer
時系列予測（ARIMA）
ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。
全国
x_by_all %>%
dplyr::select(n) %>%
ts(.$n, frequency = 7) %>%
forecast::auto.arima() %>%
forecast::forecast() %>%
plot(main = paste0("全国 @", datetime))
地方別
x_by_region %>%
dplyr::select(region, n) %>%
split(.$region) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 244.5447 241.1836 233.7635 224.3650 249.4653 249.4086 258.8122 257.6043
## [9] 260.3047 260.0289 264.6346 267.2961 272.2128 275.0328
##
## $北海道地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 231.8394 225.1136
## 46.71429 226.3835 218.5487
## 46.85714 215.2229 205.4081
## 47.00000 204.7391 194.3498
## 47.14286 228.4558 217.3341
## 47.28571 227.5860 216.0338
## 47.42857 235.5768 223.2767
## 47.57143 231.0152 216.9397
## 47.71429 231.0664 215.5886
## 47.85714 227.9759 211.0081
## 48.00000 230.5291 212.4748
## 48.14286 231.2328 212.1421
## 48.28571 234.3662 214.3315
## 48.42857 235.2231 214.1492
##
## $北海道地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 257.2500 263.9757
## 46.71429 255.9837 263.8185
## 46.85714 252.3041 262.1189
## 47.00000 243.9909 254.3802
## 47.14286 270.4748 281.5965
## 47.28571 271.2312 282.7834
## 47.42857 282.0476 294.3477
## 47.57143 284.1934 298.2689
## 47.71429 289.5430 305.0208
## 47.85714 292.0819 309.0497
## 48.00000 298.7401 316.7944
## 48.14286 303.3593 322.4500
## 48.28571 310.0594 330.0941
## 48.42857 314.8424 335.9164
##
##
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 30.16021 31.31308 32.09088 33.10684 33.17656 33.25023 32.85596 32.74969
## [9] 32.53721 32.61086 32.59437 32.71064 32.70313 32.75323
##
## $東北地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 23.21118 19.53259
## 46.71429 23.99532 20.12153
## 46.85714 24.65856 20.72413
## 47.00000 25.67205 21.73631
## 47.14286 25.66825 21.69359
## 47.28571 25.64161 21.61386
## 47.42857 24.87588 20.65148
## 47.57143 24.45831 20.06911
## 47.71429 23.85252 19.25513
## 47.85714 23.67533 18.94515
## 48.00000 23.39887 18.53107
## 48.14286 23.32919 18.36295
## 48.28571 23.10851 18.02943
## 48.42857 22.97207 17.79423
##
## $東北地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 37.10924 40.78783
## 46.71429 38.63085 42.50464
## 46.85714 39.52319 43.45762
## 47.00000 40.54164 44.47738
## 47.14286 40.68487 44.65953
## 47.28571 40.85885 44.88661
## 47.42857 40.83604 45.06045
## 47.57143 41.04108 45.43027
## 47.71429 41.22189 45.81929
## 47.85714 41.54639 46.27657
## 48.00000 41.78987 46.65767
## 48.14286 42.09209 47.05833
## 48.28571 42.29775 47.37684
## 48.42857 42.53439 47.71222
##
##
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 837.7119 618.9400 663.7576 838.6978 995.9414 1024.3698 1091.3512
## [8] 877.7446 702.8417 758.1701 914.4819 1039.8996 1040.6882 1094.9600
##
## $関東地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 762.9269 723.3382
## 46.71429 523.7645 473.3816
## 46.85714 560.8419 506.3615
## 47.00000 732.7828 676.7147
## 47.14286 888.0014 830.8614
## 47.28571 913.5113 854.8263
## 47.42857 975.4883 914.1541
## 47.57143 747.2439 678.1611
## 47.71429 559.7140 483.9467
## 47.85714 605.9662 525.3943
## 48.00000 756.2428 672.4761
## 48.14286 877.3792 791.3460
## 48.28571 874.4808 786.4959
## 48.42857 924.8887 834.8584
##
## $関東地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 912.4968 952.0856
## 46.71429 714.1156 764.4985
## 46.85714 766.6734 821.1538
## 47.00000 944.6128 1000.6808
## 47.14286 1103.8813 1161.0213
## 47.28571 1135.2283 1193.9133
## 47.42857 1207.2141 1268.5482
## 47.57143 1008.2452 1077.3281
## 47.71429 845.9694 921.7366
## 47.85714 910.3740 990.9459
## 48.00000 1072.7209 1156.4876
## 48.14286 1202.4201 1288.4532
## 48.28571 1206.8957 1294.8806
## 48.42857 1265.0312 1355.0615
##
##
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 308.3304 268.5270 238.6816 252.9532 272.3844 284.3978 302.7766 303.9728
## [9] 309.1036 297.0219 294.0318 281.9069 284.6231 281.6685
##
## $中部地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 285.0264 272.6900
## 46.71429 238.6924 222.8990
## 46.85714 204.7945 186.8557
## 47.00000 217.4075 198.5907
## 47.14286 235.3519 215.7480
## 47.28571 246.2719 226.0892
## 47.42857 262.6568 241.4186
## 47.57143 257.6551 233.1360
## 47.71429 256.5319 228.7021
## 47.85714 239.4431 208.9628
## 48.00000 232.2373 199.5253
## 48.14286 217.0397 182.7011
## 48.28571 217.0545 181.2859
## 48.42857 211.8087 174.8272
##
## $中部地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 331.6344 343.9708
## 46.71429 298.3615 314.1550
## 46.85714 272.5687 290.5075
## 47.00000 288.4989 307.3157
## 47.14286 309.4169 329.0208
## 47.28571 322.5238 342.7065
## 47.42857 342.8964 364.1346
## 47.57143 350.2906 374.8097
## 47.71429 361.6753 389.5051
## 47.85714 354.6006 385.0810
## 48.00000 355.8263 388.5383
## 48.14286 346.7741 381.1127
## 48.28571 352.1917 387.9604
## 48.42857 351.5282 388.5098
##
##
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 691.2206 477.7946 511.1281 600.5186 674.6431 670.3422 745.7933 753.9389
## [9] 578.6838 560.3746 646.4015 712.7854 704.0852 770.0854
##
## $近畿地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 647.3941 624.1938
## 46.71429 425.2986 397.5089
## 46.85714 455.9518 426.7432
## 47.00000 542.7862 512.2245
## 47.14286 614.4631 582.6058
## 47.28571 607.8104 574.7081
## 47.42857 680.9949 646.6927
## 47.57143 678.3475 638.3319
## 47.71429 496.8098 453.4684
## 47.85714 474.6239 429.2303
## 48.00000 556.9419 509.5849
## 48.14286 619.7647 570.5226
## 48.28571 607.6349 556.5771
## 48.42857 670.3231 617.5122
##
## $近畿地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 735.0471 758.2475
## 46.71429 530.2906 558.0803
## 46.85714 566.3045 595.5131
## 47.00000 658.2510 688.8126
## 47.14286 734.8231 766.6804
## 47.28571 732.8741 765.9764
## 47.42857 810.5917 844.8939
## 47.57143 829.5302 869.5458
## 47.71429 660.5578 703.8992
## 47.85714 646.1253 691.5189
## 48.00000 735.8610 783.2180
## 48.14286 805.8060 855.0482
## 48.28571 800.5356 851.5934
## 48.42857 869.8476 922.6585
##
##
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 38.16362 36.48386 36.48386 36.48386 36.48386 36.48386 36.48386 36.48386
## [9] 36.48386 36.48386 36.48386 36.48386 36.48386 36.48386
##
## $中国地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 28.60438 23.54402
## 46.71429 26.27310 20.86786
## 46.85714 25.99847 20.44784
## 47.00000 25.73085 20.03855
## 47.14286 25.46973 19.63920
## 47.28571 25.21466 19.24911
## 47.42857 24.96524 18.86765
## 47.57143 24.72110 18.49427
## 47.71429 24.48193 18.12849
## 47.85714 24.24743 17.76986
## 48.00000 24.01734 17.41797
## 48.14286 23.79143 17.07246
## 48.28571 23.56946 16.73299
## 48.42857 23.35125 16.39926
##
## $中国地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 47.72287 52.78322
## 46.71429 46.69461 52.09986
## 46.85714 46.96925 52.51988
## 47.00000 47.23687 52.92917
## 47.14286 47.49799 53.32851
## 47.28571 47.75306 53.71861
## 47.42857 48.00248 54.10007
## 47.57143 48.24662 54.47345
## 47.71429 48.48579 54.83923
## 47.85714 48.72029 55.19786
## 48.00000 48.95037 55.54975
## 48.14286 49.17629 55.89526
## 48.28571 49.39826 56.23472
## 48.42857 49.61647 56.56845
##
##
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 15.37885 16.26247 16.26247 16.26247 16.26247 16.26247 16.26247 16.26247
## [9] 16.26247 16.26247 16.26247 16.26247 16.26247 16.26247
##
## $四国地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 11.567415 9.549764
## 46.71429 11.986850 9.723472
## 46.85714 11.767950 9.388694
## 47.00000 11.559227 9.069480
## 47.14286 11.359382 8.763843
## 47.28571 11.167369 8.470185
## 47.42857 10.982334 8.187198
## 47.57143 10.803567 7.913798
## 47.71429 10.630472 7.649072
## 47.85714 10.462540 7.392243
## 48.00000 10.299336 7.142643
## 48.14286 10.140481 6.899696
## 48.28571 9.985645 6.662894
## 48.42857 9.834538 6.431796
##
## $四国地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 19.19028 21.20794
## 46.71429 20.53809 22.80147
## 46.85714 20.75699 23.13625
## 47.00000 20.96571 23.45546
## 47.14286 21.16556 23.76110
## 47.28571 21.35757 24.05476
## 47.42857 21.54261 24.33774
## 47.57143 21.72137 24.61114
## 47.71429 21.89447 24.87587
## 47.85714 22.06240 25.13270
## 48.00000 22.22561 25.38230
## 48.14286 22.38446 25.62525
## 48.28571 22.53930 25.86205
## 48.42857 22.69040 26.09315
##
##
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 134.2299 118.7607 116.4220 143.6406 184.4994 180.0764 187.9184 170.8248
## [9] 160.3751 157.6838 174.3516 199.5447 194.8698 201.1837
##
## $九州地方$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 111.48128 99.43887
## 46.71429 90.87222 76.10893
## 46.85714 84.95820 68.30228
## 47.00000 111.23180 94.07564
## 47.14286 150.36134 132.28974
## 47.28571 142.30572 122.31114
## 47.42857 147.11068 125.50835
## 47.57143 123.89655 99.05425
## 47.71429 108.47591 81.00212
## 47.85714 101.37483 71.56669
## 48.00000 115.31136 84.05736
## 48.14286 137.29108 104.33601
## 48.28571 128.89212 93.96563
## 48.42857 131.88772 95.20463
##
## $九州地方$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 156.9786 169.0210
## 46.71429 146.6493 161.4126
## 46.85714 147.8858 164.5417
## 47.00000 176.0493 193.2055
## 47.14286 218.6375 236.7091
## 47.28571 217.8471 237.8416
## 47.42857 228.7262 250.3285
## 47.57143 217.7530 242.5953
## 47.71429 212.2744 239.7481
## 47.85714 213.9927 243.8008
## 48.00000 233.3918 264.6458
## 48.14286 261.7983 294.7534
## 48.28571 260.8475 295.7740
## 48.42857 270.4797 307.1628
都道府県別
x_by_pref %>%
dplyr::select(pref, n) %>%
split(.$pref) %>%
purrr::map(., ~ ts(.$n, frequency = 7)) %>%
purrr::map(., forecast::auto.arima) %>%
purrr::map(., forecast::forecast) %>%
purrr::map2(., paste0(names(.), " @", datetime),
function(.x, name) {
plot(.x, main = name)
} )
## $北海道
## $北海道$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 244.5447 241.1836 233.7635 224.3650 249.4653 249.4086 258.8122 257.6043
## [9] 260.3047 260.0289 264.6346 267.2961 272.2128 275.0328
##
## $北海道$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 231.8394 225.1136
## 46.71429 226.3835 218.5487
## 46.85714 215.2229 205.4081
## 47.00000 204.7391 194.3498
## 47.14286 228.4558 217.3341
## 47.28571 227.5860 216.0338
## 47.42857 235.5768 223.2767
## 47.57143 231.0152 216.9397
## 47.71429 231.0664 215.5886
## 47.85714 227.9759 211.0081
## 48.00000 230.5291 212.4748
## 48.14286 231.2328 212.1421
## 48.28571 234.3662 214.3315
## 48.42857 235.2231 214.1492
##
## $北海道$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 257.2500 263.9757
## 46.71429 255.9837 263.8185
## 46.85714 252.3041 262.1189
## 47.00000 243.9909 254.3802
## 47.14286 270.4748 281.5965
## 47.28571 271.2312 282.7834
## 47.42857 282.0476 294.3477
## 47.57143 284.1934 298.2689
## 47.71429 289.5430 305.0208
## 47.85714 292.0819 309.0497
## 48.00000 298.7401 316.7944
## 48.14286 303.3593 322.4500
## 48.28571 310.0594 330.0941
## 48.42857 314.8424 335.9164
##
##
## $青森県
## $青森県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] -0.1366241 1.2221069 0.4054912 0.8962883 0.6013126 0.7785970
## [7] 0.6720467 0.7360848 0.6975970 0.7207287 0.7068263 0.7151818
## [13] 0.7101600 0.7131782
##
## $青森県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -2.659812 -3.995507
## 46.71429 -1.721406 -3.279608
## 46.85714 -2.618670 -4.219565
## 47.00000 -2.352791 -4.072750
## 47.14286 -2.767691 -4.551134
## 47.28571 -2.756048 -4.627175
## 47.42857 -2.991194 -4.930396
## 47.57143 -3.068275 -5.082182
## 47.71429 -3.232916 -5.313604
## 47.85714 -3.337751 -5.486180
## 48.00000 -3.472345 -5.684665
## 48.14286 -3.583252 -5.858706
## 48.28571 -3.703167 -6.039442
## 48.42857 -3.812799 -6.208706
##
## $青森県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 2.386564 3.722259
## 46.71429 4.165620 5.723822
## 46.85714 3.429653 5.030547
## 47.00000 4.145367 5.865326
## 47.14286 3.970316 5.753759
## 47.28571 4.313241 6.184369
## 47.42857 4.335287 6.274490
## 47.57143 4.540445 6.554352
## 47.71429 4.628110 6.708798
## 47.85714 4.779208 6.927637
## 48.00000 4.885998 7.098317
## 48.14286 5.013616 7.289070
## 48.28571 5.123487 7.459762
## 48.42857 5.239155 7.635063
##
##
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 3.550842 6.592140 7.953370 6.946003 6.953922 5.340145 5.182220 5.720799
## [9] 6.342327 6.351887 6.255508 6.152060 6.223535 6.262460
##
## $岩手県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 1.923994 1.062793
## 46.71429 4.768456 3.803056
## 46.85714 5.968668 4.918030
## 47.00000 4.952094 3.896581
## 47.14286 4.888470 3.795086
## 47.28571 3.231089 2.114622
## 47.42857 2.989667 1.828999
## 47.57143 3.395544 2.164628
## 47.71429 3.922853 2.642061
## 47.85714 3.862409 2.544559
## 48.00000 3.710676 2.363523
## 48.14286 3.545955 2.166366
## 48.28571 3.556273 2.144310
## 48.42857 3.533883 2.089461
##
## $岩手県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 5.177689 6.038890
## 46.71429 8.415825 9.381225
## 46.85714 9.938073 10.988711
## 47.00000 8.939913 9.995425
## 47.14286 9.019374 10.112758
## 47.28571 7.449202 8.565669
## 47.42857 7.374774 8.535442
## 47.57143 8.046054 9.276969
## 47.71429 8.761801 10.042593
## 47.85714 8.841366 10.159216
## 48.00000 8.800341 10.147494
## 48.14286 8.758165 10.137753
## 48.28571 8.890796 10.302760
## 48.42857 8.991037 10.435459
##
##
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 14.74829 16.17110 16.14565 16.45813 15.94360 15.80832 15.40029 15.42126
## [9] 15.38707 15.61837 15.74090 15.91509 15.92755 15.92035
##
## $宮城県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 9.739193 7.087539
## 46.71429 11.114888 8.438294
## 46.85714 10.914558 8.145383
## 47.00000 11.220920 8.448508
## 47.14286 10.483119 7.592514
## 47.28571 10.212625 7.250445
## 47.42857 9.486816 6.356412
## 47.57143 9.348182 6.133287
## 47.71429 9.147668 5.844730
## 47.85714 9.320417 5.986480
## 48.00000 9.389954 6.027967
## 48.14286 9.538427 6.162824
## 48.28571 9.507591 6.109072
## 48.42857 9.451714 6.027425
##
## $宮城県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 19.75738 22.40903
## 46.71429 21.22730 23.90390
## 46.85714 21.37675 24.14592
## 47.00000 21.69534 24.46775
## 47.14286 21.40409 24.29469
## 47.28571 21.40401 24.36619
## 47.42857 21.31376 24.44417
## 47.57143 21.49435 24.70924
## 47.71429 21.62646 24.92940
## 47.85714 21.91633 25.25027
## 48.00000 22.09184 25.45383
## 48.14286 22.29176 25.66736
## 48.28571 22.34750 25.74602
## 48.42857 22.38899 25.81328
##
##
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 1.385698 1.385698 1.385698 1.385698 1.385698 1.385698 1.385698 1.385698
## [9] 1.385698 1.385698 1.385698 1.385698 1.385698 1.385698
##
## $秋田県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 0.019641997 -0.7035045
## 46.71429 0.014372068 -0.7115642
## 46.85714 0.009122314 -0.7195930
## 47.00000 0.003892505 -0.7275913
## 47.14286 -0.001317585 -0.7355594
## 47.28571 -0.006508177 -0.7434977
## 47.42857 -0.011679489 -0.7514066
## 47.57143 -0.016831733 -0.7592862
## 47.71429 -0.021965120 -0.7671371
## 47.85714 -0.027079855 -0.7749594
## 48.00000 -0.032176139 -0.7827535
## 48.14286 -0.037254171 -0.7905197
## 48.28571 -0.042314146 -0.7982582
## 48.42857 -0.047356254 -0.8059695
##
## $秋田県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 2.751754 3.474901
## 46.71429 2.757024 3.482961
## 46.85714 2.762274 3.490989
## 47.00000 2.767504 3.498988
## 47.14286 2.772714 3.506956
## 47.28571 2.777905 3.514894
## 47.42857 2.783076 3.522803
## 47.57143 2.788228 3.530683
## 47.71429 2.793362 3.538534
## 47.85714 2.798476 3.546356
## 48.00000 2.803573 3.554150
## 48.14286 2.808651 3.561916
## 48.28571 2.813711 3.569655
## 48.42857 2.818753 3.577366
##
##
## $山形県
## $山形県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 3.362033 3.533225 3.445147 3.153105 2.994187 2.395466 2.192752 2.435595
## [9] 2.361490 2.289640 2.219976 2.152431 2.086942 2.023445
##
## $山形県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 2.2654856 1.68500844
## 46.71429 2.3738796 1.76015920
## 46.85714 2.2297218 1.58631463
## 47.00000 1.8872241 1.21710732
## 47.14286 1.6826429 0.98835363
## 47.28571 1.0424000 0.32613021
## 47.42857 0.8017819 0.06544693
## 47.57143 1.0357272 0.29468174
## 47.71429 0.9394845 0.18672006
## 47.85714 0.8471329 0.08351574
## 48.00000 0.7584582 -0.01522251
## 48.14286 0.6732651 -0.10975817
## 48.28571 0.5913747 -0.20033083
## 48.42857 0.5126219 -0.28715949
##
## $山形県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.458580 5.039058
## 46.71429 4.692571 5.306291
## 46.85714 4.660572 5.303979
## 47.00000 4.418985 5.089102
## 47.14286 4.305730 5.000020
## 47.28571 3.748532 4.464802
## 47.42857 3.583722 4.320057
## 47.57143 3.835463 4.576509
## 47.71429 3.783496 4.536261
## 47.85714 3.732147 4.495764
## 48.00000 3.681494 4.455174
## 48.14286 3.631598 4.414621
## 48.28571 3.582509 4.374215
## 48.42857 3.534268 4.334050
##
##
## $福島県
## $福島県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 4.296749 4.403584 4.109611 4.078718 4.105793 4.082063 4.102861 4.084633
## [9] 4.100609 4.086607 4.098879 4.088123 4.097550 4.089288
##
## $福島県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 1.757228 0.4128863
## 46.71429 1.768681 0.3738476
## 46.85714 1.280958 -0.2164406
## 47.00000 1.201570 -0.3214994
## 47.14286 1.224700 -0.3004585
## 47.28571 1.161316 -0.3848343
## 47.42857 1.175851 -0.3736149
## 47.57143 1.124134 -0.4430597
## 47.71429 1.131753 -0.4398641
## 47.85714 1.088654 -0.4983659
## 48.00000 1.090803 -0.5015761
## 48.14286 1.054139 -0.5519557
## 48.28571 1.052014 -0.5601946
## 48.42857 1.020198 -0.6044802
##
## $福島県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 6.836270 8.180611
## 46.71429 7.038487 8.433321
## 46.85714 6.938263 8.435662
## 47.00000 6.955865 8.478935
## 47.14286 6.986887 8.512045
## 47.28571 7.002810 8.548961
## 47.42857 7.029872 8.579337
## 47.57143 7.045132 8.612326
## 47.71429 7.069465 8.641082
## 47.85714 7.084560 8.671580
## 48.00000 7.106955 8.699334
## 48.14286 7.122108 8.728202
## 48.28571 7.143085 8.755294
## 48.42857 7.158378 8.783056
##
##
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 50.49876 42.13571 50.96074 50.52178 50.09432 50.98577 59.00257 53.87831
## [9] 50.43009 47.21101 50.33936 53.55331 51.71885 54.72258
##
## $茨城県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 44.94296 42.00189
## 46.71429 36.10587 32.91386
## 46.85714 44.87480 41.65310
## 47.00000 44.19923 40.85228
## 47.14286 43.42301 39.89142
## 47.28571 44.05387 40.38435
## 47.42857 51.84846 48.06130
## 47.57143 46.10766 41.99413
## 47.71429 42.30523 38.00419
## 47.85714 38.83712 34.40425
## 48.00000 41.67878 37.09414
## 48.14286 44.59135 39.84717
## 48.28571 42.47782 37.58591
## 48.42857 45.21676 40.18468
##
## $茨城県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 56.05457 58.99564
## 46.71429 48.16555 51.35755
## 46.85714 57.04667 60.26837
## 47.00000 56.84433 60.19129
## 47.14286 56.76564 60.29722
## 47.28571 57.91766 61.58719
## 47.42857 66.15668 69.94384
## 47.57143 61.64895 65.76248
## 47.71429 58.55496 62.85600
## 47.85714 55.58491 60.01778
## 48.00000 58.99995 63.58459
## 48.14286 62.51526 67.25944
## 48.28571 60.95988 65.85179
## 48.42857 64.22840 69.26048
##
##
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 9.668725 7.490071 9.081006 9.131094 7.596483 8.493873 9.193992 8.609680
## [9] 8.609680 8.609680 8.609680 8.609680 8.609680 8.609680
##
## $栃木県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 6.460687 4.762455
## 46.71429 4.216144 2.483031
## 46.85714 5.742490 3.975186
## 47.00000 5.729215 3.928368
## 47.14286 4.132400 2.298624
## 47.28571 4.968682 3.102559
## 47.42857 5.608735 3.710816
## 47.57143 4.826750 2.824188
## 47.71429 4.749928 2.706698
## 47.85714 4.674604 2.591501
## 48.00000 4.600696 2.478468
## 48.14286 4.528126 2.367482
## 48.28571 4.456824 2.258435
## 48.42857 4.386726 2.151228
##
## $栃木県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 12.87676 14.57499
## 46.71429 10.76400 12.49711
## 46.85714 12.41952 14.18683
## 47.00000 12.53297 14.33382
## 47.14286 11.06057 12.89434
## 47.28571 12.01906 13.88519
## 47.42857 12.77925 14.67717
## 47.57143 12.39261 14.39517
## 47.71429 12.46943 14.51266
## 47.85714 12.54476 14.62786
## 48.00000 12.61866 14.74089
## 48.14286 12.69123 14.85188
## 48.28571 12.76254 14.96093
## 48.42857 12.83263 15.06813
##
##
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 23.90564 25.58457 24.31552 25.04962 24.72949 24.80569 24.83857 24.77755
## [9] 24.82964 24.79674 24.81277 24.80760 24.80736 24.80942
##
## $群馬県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 19.14601 16.62641
## 46.71429 19.84667 16.80921
## 46.85714 18.20286 14.96702
## 47.00000 18.78676 15.47140
## 47.14286 17.97708 14.40257
## 47.28571 17.87206 14.20161
## 47.42857 17.57705 13.73303
## 47.57143 17.27582 13.30464
## 47.71429 17.07490 12.96979
## 47.85714 16.79423 12.55795
## 48.00000 16.57949 12.22106
## 48.14286 16.34106 11.85915
## 48.28571 16.11959 11.52056
## 48.42857 15.90299 11.18821
##
## $群馬県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 28.66527 31.18487
## 46.71429 31.32247 34.35993
## 46.85714 30.42818 33.66402
## 47.00000 31.31249 34.62785
## 47.14286 31.48190 35.05640
## 47.28571 31.73933 35.40977
## 47.42857 32.10010 35.94411
## 47.57143 32.27927 36.25045
## 47.71429 32.58438 36.68949
## 47.85714 32.79925 37.03552
## 48.00000 33.04604 37.40447
## 48.14286 33.27413 37.75605
## 48.28571 33.49513 38.09416
## 48.42857 33.71586 38.43064
##
##
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 92.71813 81.10044 73.07240 102.37432 131.91117 113.14055 107.80500
## [8] 88.54207 79.77138 81.63128 101.12027 119.85941 114.02571 102.28216
##
## $埼玉県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 77.20390 68.99116
## 46.71429 65.02395 56.51357
## 46.85714 56.70931 48.04721
## 47.00000 85.70623 76.88268
## 47.14286 114.66415 105.53413
## 47.28571 94.92138 85.27673
## 47.42857 88.47113 78.23640
## 47.57143 66.41264 54.69803
## 47.71429 56.88186 44.76487
## 47.85714 58.32059 45.98065
## 48.00000 77.40024 64.84362
## 48.14286 95.45974 82.54334
## 48.28571 88.53096 75.03486
## 48.42857 75.49873 61.32044
##
## $埼玉県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 108.23236 116.44510
## 46.71429 97.17694 105.68732
## 46.85714 89.43549 98.09759
## 47.00000 119.04241 127.86597
## 47.14286 149.15819 158.28821
## 47.28571 131.35972 141.00437
## 47.42857 127.13887 137.37361
## 47.57143 110.67151 122.38612
## 47.71429 102.66090 114.77788
## 47.85714 104.94198 117.28192
## 48.00000 124.84030 137.39693
## 48.14286 144.25908 157.17548
## 48.28571 139.52047 153.01657
## 48.42857 129.06560 143.24389
##
##
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 92.86451 87.66996 81.75183 85.38599 91.92568 96.46858 98.27620 91.72949
## [9] 86.40565 82.62420 88.40951 88.54210 94.10026 93.98277
##
## $千葉県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 80.37022 73.75614
## 46.71429 73.60131 66.15381
## 46.85714 66.98285 59.16462
## 47.00000 70.12388 62.04462
## 47.14286 76.23623 67.93074
## 47.28571 80.37860 71.86108
## 47.42857 81.80032 73.07851
## 47.57143 74.05211 64.69428
## 47.71429 68.02937 58.30156
## 47.85714 63.68914 53.66552
## 48.00000 68.96596 58.67317
## 48.14286 68.61374 58.06431
## 48.28571 73.70200 62.90382
## 48.42857 73.12630 62.08555
##
## $千葉県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 105.35880 111.9729
## 46.71429 101.73862 109.1861
## 46.85714 96.52081 104.3390
## 47.00000 100.64809 108.7274
## 47.14286 107.61513 115.9206
## 47.28571 112.55855 121.0761
## 47.42857 114.75208 123.4739
## 47.57143 109.40686 118.7647
## 47.71429 104.78192 114.5097
## 47.85714 101.55926 111.5829
## 48.00000 107.85305 118.1458
## 48.14286 108.47046 119.0199
## 48.28571 114.49851 125.2967
## 48.42857 114.83925 125.8800
##
##
## $東京都
## $東京都$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 433.3874 357.8701 348.9340 440.9279 511.1238 522.1209 523.1548 403.8172
## [9] 333.7538 363.7776 426.9269 493.5477 487.7809 491.7347
##
## $東京都$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 384.2038 358.1675
## 46.71429 302.1775 272.6957
## 46.85714 289.2068 257.5891
## 47.00000 378.9261 346.1044
## 47.14286 448.7087 415.6681
## 47.28571 458.9502 425.5097
## 47.42857 458.8718 424.8425
## 47.57143 332.1026 294.1392
## 47.71429 257.8879 217.7269
## 47.85714 284.1447 241.9895
## 48.00000 343.9786 300.0684
## 48.14286 408.1515 362.9455
## 48.28571 399.7018 353.0755
## 48.42857 400.8307 352.7090
##
## $東京都$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 482.5710 508.6072
## 46.71429 413.5627 443.0445
## 46.85714 408.6613 440.2789
## 47.00000 502.9297 535.7515
## 47.14286 573.5389 606.5795
## 47.28571 585.2915 618.7320
## 47.42857 587.4377 621.4671
## 47.57143 475.5317 513.4951
## 47.71429 409.6198 449.7808
## 47.85714 443.4106 485.5658
## 48.00000 509.8753 553.7854
## 48.14286 578.9439 624.1499
## 48.28571 575.8600 622.4862
## 48.42857 582.6387 630.7604
##
##
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 167.87795 112.35500 97.43461 158.00325 209.52666 210.40320 202.65446
## [8] 173.92961 141.11721 131.53766 155.64453 185.32431 191.33001 188.87389
##
## $神奈川県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 145.79679 134.10773
## 46.71429 86.77282 73.23043
## 46.85714 70.97411 56.96676
## 47.00000 131.52399 117.50671
## 47.14286 183.00033 168.95814
## 47.28571 183.85504 169.80130
## 47.42857 175.27385 160.77943
## 47.57143 141.70938 124.65301
## 47.71429 105.93038 87.30359
## 47.85714 95.16720 75.91384
## 48.00000 118.91283 99.46824
## 48.14286 148.39005 128.83823
## 48.28571 154.13053 134.43832
## 48.42857 150.82437 130.68218
##
## $神奈川県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 189.9591 201.6482
## 46.71429 137.9372 151.4796
## 46.85714 123.8951 137.9025
## 47.00000 184.4825 198.4998
## 47.14286 236.0530 250.0952
## 47.28571 236.9514 251.0051
## 47.42857 230.0351 244.5295
## 47.57143 206.1498 223.2062
## 47.71429 176.3040 194.9308
## 47.85714 167.9081 187.1615
## 48.00000 192.3762 211.8208
## 48.14286 222.2586 241.8104
## 48.28571 228.5295 248.2217
## 48.42857 226.9234 247.0656
##
##
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 7.351577 4.230094 5.798325 3.769424 5.667883 3.891480 5.553674 3.998346
## [9] 5.453679 4.091912 5.366128 4.173834 5.289473 4.245561
##
## $新潟県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.3417172 2.74839350
## 46.71429 1.0983899 -0.55943391
## 46.85714 2.6666011 1.00876643
## 47.00000 0.6001370 -1.07758213
## 47.14286 2.3997862 0.66976023
## 47.28571 0.5843667 -1.16631323
## 47.42857 2.1560903 0.35751790
## 47.57143 0.5605906 -1.25924709
## 47.71429 1.9323881 0.06832923
## 47.85714 0.5295570 -1.35623985
## 48.00000 1.7260683 -0.20086292
## 48.14286 0.4920442 -1.45697774
## 48.28571 1.5349284 -0.45260748
## 48.42857 0.4488153 -1.56106051
##
## $新潟県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 10.361437 11.954761
## 46.71429 7.361798 9.019621
## 46.85714 8.930050 10.587884
## 47.00000 6.938711 8.616430
## 47.14286 8.935980 10.666006
## 47.28571 7.198593 8.949273
## 47.42857 8.951258 10.749831
## 47.57143 7.436101 9.255939
## 47.71429 8.974970 10.839029
## 47.85714 7.654267 9.540064
## 48.00000 9.006188 10.933119
## 48.14286 7.855624 9.804646
## 48.28571 9.044018 11.031554
## 48.42857 8.042307 10.052182
##
##
## $富山県
## $富山県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.140912 2.104518 2.069935 2.037073 2.005846 1.976172 1.947975 1.921181
## [9] 1.895720 1.871526 1.848535 1.826689 1.805929 1.786202
##
## $富山県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -0.3019166 -1.595072
## 46.71429 -0.4914177 -1.865623
## 46.85714 -0.6568753 -2.100362
## 47.00000 -0.8027359 -2.306040
## 47.14286 -0.9322653 -2.487607
## 47.28571 -1.0479584 -2.648836
## 47.42857 -1.1517781 -2.792688
## 47.57143 -1.2453056 -2.921542
## 47.71429 -1.3298386 -3.037346
## 47.85714 -1.4064585 -3.141718
## 48.00000 -1.4760773 -3.236021
## 48.14286 -1.5394728 -3.321411
## 48.28571 -1.5973140 -3.398882
## 48.42857 -1.6501804 -3.469291
##
## $富山県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.583740 5.876896
## 46.71429 4.700454 6.074659
## 46.85714 4.796746 6.240232
## 47.00000 4.876882 6.380186
## 47.14286 4.943957 6.499299
## 47.28571 5.000303 6.601181
## 47.42857 5.047728 6.688639
## 47.57143 5.087668 6.763904
## 47.71429 5.121279 6.828786
## 47.85714 5.149510 6.884770
## 48.00000 5.173148 6.933091
## 48.14286 5.192850 6.974788
## 48.28571 5.209172 7.010740
## 48.42857 5.222585 7.041696
##
##
## $石川県
## $石川県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 1.781732 1.781732 1.781732 1.781732 1.781732 1.781732 1.781732 1.781732
## [9] 1.781732 1.781732 1.781732 1.781732 1.781732 1.781732
##
## $石川県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -1.841537 -3.759580
## 46.71429 -2.043911 -4.069084
## 46.85714 -2.236104 -4.363019
## 47.00000 -2.419515 -4.643520
## 47.14286 -2.595246 -4.912278
## 47.28571 -2.764189 -5.170655
## 47.42857 -2.927075 -5.419767
## 47.57143 -3.084512 -5.660546
## 47.71429 -3.237012 -5.893775
## 47.85714 -3.385013 -6.120123
## 48.00000 -3.528891 -6.340166
## 48.14286 -3.668973 -6.554402
## 48.28571 -3.805543 -6.763269
## 48.42857 -3.938855 -6.967150
##
## $石川県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 5.405002 7.323045
## 46.71429 5.607376 7.632549
## 46.85714 5.799569 7.926484
## 47.00000 5.982979 8.206985
## 47.14286 6.158711 8.475743
## 47.28571 6.327654 8.734120
## 47.42857 6.490540 8.983232
## 47.57143 6.647977 9.224011
## 47.71429 6.800477 9.457240
## 47.85714 6.948478 9.683588
## 48.00000 7.092356 9.903631
## 48.14286 7.232438 10.117867
## 48.28571 7.369008 10.326734
## 48.42857 7.502320 10.530615
##
##
## $福井県
## $福井県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.651862 1.781483 1.821262 1.912913 1.453001 1.588396 1.482998 1.301418
## [9] 1.378148 1.258312 1.208247 1.224888 1.145473 1.139441
##
## $福井県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 0.6870396 -0.3530748
## 46.71429 -0.3488463 -1.4765747
## 46.85714 -0.5097529 -1.7437176
## 47.00000 -0.6501624 -2.0069723
## 47.14286 -1.1550298 -2.5356378
## 47.28571 -1.1100201 -2.5384755
## 47.42857 -1.2714953 -2.7296362
## 47.57143 -1.4732948 -2.9421391
## 47.71429 -1.4324848 -2.9203440
## 47.85714 -1.5675432 -3.0634605
## 48.00000 -1.6278292 -3.1291571
## 48.14286 -1.6237401 -3.1317125
## 48.28571 -1.7080365 -3.2185932
## 48.42857 -1.7189165 -3.2320397
##
## $福井県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.616685 5.656799
## 46.71429 3.911813 5.039541
## 46.85714 4.152277 5.386241
## 47.00000 4.475988 5.832798
## 47.14286 4.061032 5.441640
## 47.28571 4.286813 5.715268
## 47.42857 4.237492 5.695633
## 47.57143 4.076131 5.544975
## 47.71429 4.188781 5.676640
## 47.85714 4.084167 5.580085
## 48.00000 4.044322 5.545650
## 48.14286 4.073515 5.581488
## 48.28571 3.998983 5.509539
## 48.42857 3.997799 5.510922
##
##
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.598580 2.828837 3.033621 3.463276 3.875714 3.646932 3.832411 3.625595
## [9] 3.558295 3.536395 3.529268 3.526949 3.526194 3.525949
##
## $山梨県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 0.7023626 -0.30143400
## 46.71429 0.7185710 -0.39853648
## 46.85714 0.8368589 -0.32603672
## 47.00000 1.2086250 0.01508491
## 47.14286 1.5717360 0.35208360
## 47.28571 1.2968295 0.05276035
## 47.42857 1.4377543 0.17009964
## 47.57143 1.2184663 -0.05579053
## 47.71429 1.1253065 -0.16263965
## 47.85714 1.0724610 -0.23186656
## 48.00000 1.0329446 -0.28852914
## 48.14286 0.9980521 -0.34066510
## 48.28571 0.9649447 -0.39089902
## 48.42857 0.9326876 -0.44010190
##
## $山梨県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.494796 5.498593
## 46.71429 4.939104 6.056211
## 46.85714 5.230383 6.393279
## 47.00000 5.717927 6.911467
## 47.14286 6.179693 7.399345
## 47.28571 5.997035 7.241104
## 47.42857 6.227068 7.494722
## 47.57143 6.032723 7.306980
## 47.71429 5.991283 7.279229
## 47.85714 6.000328 7.304656
## 48.00000 6.025591 7.347065
## 48.14286 6.055846 7.394563
## 48.28571 6.087444 7.443288
## 48.42857 6.119210 7.492000
##
##
## $長野県
## $長野県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 17.18564 17.18564 17.18564 17.18564 17.18564 17.18564 17.18564 17.18564
## [9] 17.18564 17.18564 17.18564 17.18564 17.18564 17.18564
##
## $長野県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 13.76553 11.955040
## 46.71429 13.49910 11.547568
## 46.85714 13.25067 11.167622
## 47.00000 13.01701 10.810279
## 47.14286 12.79578 10.471929
## 47.28571 12.58517 10.149832
## 47.42857 12.38379 9.841848
## 47.57143 12.19052 9.546271
## 47.71429 12.00446 9.261712
## 47.85714 11.82485 8.987023
## 48.00000 11.65107 8.721244
## 48.14286 11.48258 8.463559
## 48.28571 11.31892 8.213273
## 48.42857 11.15971 7.969781
##
## $長野県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 20.60574 22.41623
## 46.71429 20.87217 22.82370
## 46.85714 21.12060 23.20365
## 47.00000 21.35426 23.56099
## 47.14286 21.57549 23.89934
## 47.28571 21.78610 24.22144
## 47.42857 21.98748 24.52942
## 47.57143 22.18075 24.82500
## 47.71429 22.36681 25.10956
## 47.85714 22.54642 25.38425
## 48.00000 22.72020 25.65003
## 48.14286 22.88869 25.90771
## 48.28571 23.05235 26.15800
## 48.42857 23.21156 26.40149
##
##
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 18.08505 19.65678 17.89348 23.88918 20.01803 23.86479 20.75519 21.42818
## [9] 21.42818 21.42818 21.42818 21.42818 21.42818 21.42818
##
## $岐阜県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 14.17210 12.100702
## 46.71429 15.46430 13.244937
## 46.85714 13.43899 11.080926
## 47.00000 19.18725 16.698206
## 47.14286 15.08106 12.467582
## 47.28571 18.70346 15.971219
## 47.42857 15.37885 12.532795
## 47.57143 15.44923 12.284166
## 47.71429 15.14469 11.818417
## 47.85714 14.85425 11.374221
## 48.00000 14.57611 10.948837
## 48.14286 14.30882 10.540059
## 48.28571 14.05121 10.146082
## 48.42857 13.80230 9.765407
##
## $岐阜県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 21.99801 24.06940
## 46.71429 23.84925 26.06861
## 46.85714 22.34797 24.70603
## 47.00000 28.59110 31.08015
## 47.14286 24.95500 27.56848
## 47.28571 29.02612 31.75836
## 47.42857 26.13152 28.97758
## 47.57143 27.40713 30.57220
## 47.71429 27.71167 31.03795
## 47.85714 28.00211 31.48214
## 48.00000 28.28026 31.90753
## 48.14286 28.54754 32.31631
## 48.28571 28.80515 32.71028
## 48.42857 29.05406 33.09096
##
##
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 65.57327 64.39440 57.05458 58.70239 66.33668 62.08077 69.77485 66.26273
## [9] 65.26130 64.97575 64.89433 64.87112 64.86450 64.86261
##
## $静岡県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 58.47271 54.71390
## 46.71429 56.31342 52.03561
## 46.85714 48.45765 43.90671
## 47.00000 49.70044 44.93510
## 47.14286 56.96827 52.00893
## 47.28571 52.36539 47.22238
## 47.42857 59.72604 54.40653
## 47.57143 55.11883 49.21960
## 47.71429 53.49102 47.26021
## 47.85714 52.69750 46.19779
## 48.00000 52.15096 45.40503
## 48.14286 51.68516 44.70493
## 48.28571 51.25203 44.04602
## 48.42857 50.83706 43.41237
##
## $静岡県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 72.67383 76.43264
## 46.71429 72.47537 76.75318
## 46.85714 65.65151 70.20245
## 47.00000 67.70433 72.46968
## 47.14286 75.70510 80.66444
## 47.28571 71.79615 76.93916
## 47.42857 79.82366 85.14318
## 47.57143 77.40664 83.30586
## 47.71429 77.03158 83.26239
## 47.85714 77.25400 83.75372
## 48.00000 77.63771 84.38364
## 48.14286 78.05708 85.03730
## 48.28571 78.47697 85.68298
## 48.42857 78.88817 86.31285
##
##
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 183.9640 151.0634 161.2800 201.9838 214.9861 237.0064 226.6026 206.3468
## [9] 186.1821 192.4438 217.3939 225.3638 238.8614 232.4842
##
## $愛知県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 166.5590 157.3453
## 46.71429 130.3683 119.4130
## 46.85714 136.6195 123.5650
## 47.00000 174.2814 159.6166
## 47.14286 184.4263 168.2489
## 47.28571 203.8733 186.3337
## 47.42857 191.0693 172.2591
## 47.57143 163.7490 141.1990
## 47.71429 139.2526 114.4097
## 47.85714 141.0101 113.7828
## 48.00000 162.0014 132.6784
## 48.14286 166.2167 134.9062
## 48.28571 176.2047 143.0362
## 48.42857 166.4974 131.5661
##
## $愛知県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 201.3690 210.5826
## 46.71429 171.7584 182.7137
## 46.85714 185.9405 198.9950
## 47.00000 229.6863 244.3511
## 47.14286 245.5460 261.7234
## 47.28571 270.1395 287.6791
## 47.42857 262.1359 280.9461
## 47.57143 248.9447 271.4946
## 47.71429 233.1115 257.9545
## 47.85714 243.8774 271.1047
## 48.00000 272.7865 302.1095
## 48.14286 284.5108 315.8213
## 48.28571 301.5181 334.6866
## 48.42857 298.4711 333.4024
##
##
## $三重県
## $三重県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 21.72477 21.16076 20.05151 21.88159 23.21814 21.37195 23.52049 22.53169
## [9] 22.53169 22.53169 22.53169 22.53169 22.53169 22.53169
##
## $三重県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 17.56084 15.35659
## 46.71429 16.64687 14.25736
## 46.85714 15.21291 12.65151
## 47.00000 16.73873 14.01627
## 47.14286 17.78805 14.91354
## 47.28571 15.66908 12.65016
## 47.42857 17.55730 14.40058
## 47.57143 16.10025 12.69565
## 47.71429 15.79002 12.22119
## 47.85714 15.49344 11.76762
## 48.00000 15.20887 11.33240
## 48.14286 14.93495 10.91347
## 48.28571 14.67057 10.50914
## 48.42857 14.41479 10.11796
##
## $三重県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 25.88870 28.09295
## 46.71429 25.67465 28.06416
## 46.85714 24.89012 27.45152
## 47.00000 27.02445 29.74691
## 47.14286 28.64823 31.52275
## 47.28571 27.07483 30.09374
## 47.42857 29.48368 32.64040
## 47.57143 28.96313 32.36773
## 47.71429 29.27336 32.84219
## 47.85714 29.56994 33.29576
## 48.00000 29.85451 33.73098
## 48.14286 30.12843 34.14991
## 48.28571 30.39281 34.55424
## 48.42857 30.64859 34.94542
##
##
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 9.412420 6.631821 8.323845 7.465592 7.995011 7.730405 7.896219 7.814747
## [9] 7.866739 7.841693 7.858017 7.850331 7.855463 7.853110
##
## $滋賀県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 5.501116 3.43059579
## 46.71429 2.655543 0.55062863
## 46.85714 3.921654 1.59127435
## 47.00000 2.978156 0.60265060
## 47.14286 3.344719 0.88300364
## 47.28571 2.994919 0.48810484
## 47.42857 3.056853 0.49504790
## 47.57143 2.893712 0.28867315
## 47.71429 2.859911 0.20945585
## 47.85714 2.756510 0.06457675
## 48.00000 2.694172 -0.03940202
## 48.14286 2.610859 -0.16274979
## 48.28571 2.541033 -0.27225582
## 48.42857 2.465347 -0.38676217
##
## $滋賀県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 13.32373 15.39424
## 46.71429 10.60810 12.71301
## 46.85714 12.72604 15.05642
## 47.00000 11.95303 14.32853
## 47.14286 12.64530 15.10702
## 47.28571 12.46589 14.97270
## 47.42857 12.73559 15.29739
## 47.57143 12.73578 15.34082
## 47.71429 12.87357 15.52402
## 47.85714 12.92688 15.61881
## 48.00000 13.02186 15.75544
## 48.14286 13.08980 15.86341
## 48.28571 13.16989 15.98318
## 48.42857 13.24087 16.09298
##
##
## $京都府
## $京都府$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 26.42988 23.69088 29.72365 30.26702 26.62026 27.80968 29.46297 27.33386
## [9] 25.90037 25.15997 28.30940 29.11694 28.37025 28.17009
##
## $京都府$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 19.48028 15.80138
## 46.71429 16.11553 12.10538
## 46.85714 22.03678 17.96759
## 47.00000 22.52087 18.42030
## 47.14286 18.78754 14.64114
## 47.28571 19.84359 15.62659
## 47.42857 21.32755 17.02092
## 47.57143 18.84521 14.35159
## 47.71429 17.16524 12.54115
## 47.85714 16.22372 11.49315
## 48.00000 19.18620 14.35667
## 48.14286 19.80858 14.88103
## 48.28571 18.87573 13.84963
## 48.42857 18.48945 13.36483
##
## $京都府$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 33.37948 37.05838
## 46.71429 31.26623 35.27638
## 46.85714 37.41053 41.47972
## 47.00000 38.01317 42.11374
## 47.14286 34.45298 38.59937
## 47.28571 35.77578 39.99278
## 47.42857 37.59840 41.90503
## 47.57143 35.82251 40.31613
## 47.71429 34.63550 39.25960
## 47.85714 34.09623 38.82680
## 48.00000 37.43260 42.26213
## 48.14286 38.42531 43.35286
## 48.28571 37.86478 42.89088
## 48.42857 37.85073 42.97535
##
##
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 473.0009 310.1748 324.0652 381.2045 401.6048 435.8966 486.1160 514.8899
## [9] 382.4294 359.3240 416.7148 429.0437 459.5849 504.8285
##
## $大阪府$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 440.4399 423.2031
## 46.71429 272.8985 253.1657
## 46.85714 285.2832 264.7533
## 47.00000 340.9731 319.6759
## 47.14286 359.9744 337.9366
## 47.28571 392.9128 370.1585
## 47.42857 441.8200 418.3711
## 47.57143 463.2483 435.9108
## 47.71429 327.2175 297.9900
## 47.85714 301.8656 271.4490
## 48.00000 357.0946 325.5336
## 48.14286 367.3373 334.6720
## 48.28571 395.8607 362.1271
## 48.42857 439.1483 404.3793
##
## $大阪府$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 505.5620 522.7987
## 46.71429 347.4510 367.1838
## 46.85714 362.8471 383.3770
## 47.00000 421.4359 442.7331
## 47.14286 443.2352 465.2730
## 47.28571 478.8805 501.6348
## 47.42857 530.4120 553.8609
## 47.57143 566.5315 593.8690
## 47.71429 437.6414 466.8689
## 47.85714 416.7824 447.1990
## 48.00000 476.3351 507.8961
## 48.14286 490.7501 523.4155
## 48.28571 523.3092 557.0428
## 48.42857 570.5087 605.2776
##
##
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 136.42757 99.87249 99.87249 114.02284 162.95949 115.20204 139.96516
## [8] 134.91087 113.35806 113.35806 121.70109 150.55404 122.39634 136.99663
##
## $兵庫県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 123.81488 117.13812
## 46.71429 86.54859 79.49535
## 46.85714 85.87348 78.46285
## 47.00000 99.37981 91.62825
## 47.14286 147.69959 139.62148
## 47.28571 99.34926 90.95730
## 47.42857 123.54089 114.84640
## 47.57143 114.72720 104.04261
## 47.71429 92.05100 80.77173
## 47.85714 90.98395 79.13981
## 48.00000 98.30855 85.92528
## 48.14286 126.18560 113.28572
## 48.28571 97.08960 83.69302
## 48.42857 110.78516 96.90964
##
## $兵庫県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 149.0403 155.7170
## 46.71429 113.1964 120.2496
## 46.85714 113.8715 121.2821
## 47.00000 128.6659 136.4174
## 47.14286 178.2194 186.2975
## 47.28571 131.0548 139.4468
## 47.42857 156.3894 165.0839
## 47.57143 155.0945 165.7791
## 47.71429 134.6651 145.9444
## 47.85714 135.7322 147.5763
## 48.00000 145.0936 157.4769
## 48.14286 174.9225 187.8224
## 48.28571 147.7031 161.0997
## 48.42857 163.2081 177.0836
##
##
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 18.20939 19.27064 18.57075 17.81898 19.09558 19.72714 18.64668 18.91710
## [9] 18.91710 18.91710 18.91710 18.91710 18.91710 18.91710
##
## $奈良県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 13.83752 11.523195
## 46.71429 14.72620 12.320517
## 46.85714 13.86005 11.366358
## 47.00000 12.94769 10.368990
## 47.14286 14.06883 11.407830
## 47.28571 14.54960 11.808775
## 47.42857 13.32261 10.504222
## 47.57143 13.30579 10.335338
## 47.71429 13.13586 10.075456
## 47.85714 12.97079 9.822998
## 48.00000 12.81017 9.577361
## 48.14286 12.65368 9.338021
## 48.28571 12.50100 9.104517
## 48.42857 12.35187 8.876442
##
## $奈良県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 22.58126 24.89559
## 46.71429 23.81509 26.22077
## 46.85714 23.28146 25.77515
## 47.00000 22.69027 25.26897
## 47.14286 24.12232 26.78332
## 47.28571 24.90468 27.64550
## 47.42857 23.97074 26.78913
## 47.57143 24.52841 27.49886
## 47.71429 24.69834 27.75874
## 47.85714 24.86341 28.01120
## 48.00000 25.02402 28.25683
## 48.14286 25.18052 28.49617
## 48.28571 25.33320 28.72968
## 48.42857 25.48233 28.95775
##
##
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 7.819377 8.455737 9.005494 8.629255 7.748292 7.772132 8.445503 8.760071
## [9] 8.070926 8.390933 8.642291 8.498099 7.685476 8.279884
##
## $和歌山県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 5.686298 4.557114
## 46.71429 6.138041 4.911126
## 46.85714 6.632973 5.377037
## 47.00000 6.154971 4.845165
## 47.14286 5.155174 3.782460
## 47.28571 5.074046 3.645766
## 47.42857 5.649932 4.170046
## 47.57143 5.900554 4.386817
## 47.71429 5.131308 3.575169
## 47.85714 5.367693 3.767286
## 48.00000 5.540567 3.898614
## 48.14286 5.320822 3.638874
## 48.28571 4.433972 2.712730
## 48.42857 4.955635 3.195884
##
## $和歌山県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 9.952456 11.08164
## 46.71429 10.773434 12.00035
## 46.85714 11.378014 12.63395
## 47.00000 11.103538 12.41334
## 47.14286 10.341411 11.71412
## 47.28571 10.470217 11.89850
## 47.42857 11.241074 12.72096
## 47.57143 11.619589 13.13333
## 47.71429 11.010543 12.56668
## 47.85714 11.414174 13.01458
## 48.00000 11.744014 13.38597
## 48.14286 11.675375 13.35732
## 48.28571 10.936981 12.65822
## 48.42857 11.604132 13.36388
##
##
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 0.5541348 0.3283466 0.3499678 0.2736668 0.3598942 0.4030796 0.4474737
## [8] 0.4318692 0.3761246 0.3359957 0.2438730 0.2488211 0.4290205 0.4407119
##
## $鳥取県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -0.3863168 -0.8841619
## 46.71429 -0.6251150 -1.1298470
## 46.85714 -0.6133934 -1.1233660
## 47.00000 -0.7053023 -1.2235373
## 47.14286 -0.6221139 -1.1419576
## 47.28571 -0.5810163 -1.1019652
## 47.42857 -0.5382556 -1.0600691
## 47.57143 -0.5538620 -1.0756765
## 47.71429 -0.6100795 -1.1321445
## 47.85714 -0.6506109 -1.1728889
## 48.00000 -0.7430200 -1.2654496
## 48.14286 -0.7384286 -1.2610470
## 48.28571 -0.5585764 -1.0813787
## 48.42857 -0.5472234 -1.0702048
##
## $鳥取県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 1.494586 1.992431
## 46.71429 1.281808 1.786540
## 46.85714 1.313329 1.823302
## 47.00000 1.252636 1.770871
## 47.14286 1.341902 1.861746
## 47.28571 1.387175 1.908124
## 47.42857 1.433203 1.955016
## 47.57143 1.417600 1.939415
## 47.71429 1.362329 1.884394
## 47.85714 1.322602 1.844880
## 48.00000 1.230766 1.753196
## 48.14286 1.236071 1.758689
## 48.28571 1.416617 1.939420
## 48.42857 1.428647 1.951629
##
##
## $島根県
## $島根県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803
## [8] 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803 0.4576803
##
## $島根県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -6.174715 -9.685693
## 46.71429 -6.174715 -9.685693
## 46.85714 -6.174715 -9.685693
## 47.00000 -6.174715 -9.685693
## 47.14286 -6.174715 -9.685693
## 47.28571 -6.174715 -9.685693
## 47.42857 -6.174715 -9.685693
## 47.57143 -6.174715 -9.685693
## 47.71429 -6.174715 -9.685693
## 47.85714 -6.174715 -9.685693
## 48.00000 -6.174715 -9.685693
## 48.14286 -6.174715 -9.685693
## 48.28571 -6.174715 -9.685693
## 48.42857 -6.174715 -9.685693
##
## $島根県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 7.090076 10.60105
## 46.71429 7.090076 10.60105
## 46.85714 7.090076 10.60105
## 47.00000 7.090076 10.60105
## 47.14286 7.090076 10.60105
## 47.28571 7.090076 10.60105
## 47.42857 7.090076 10.60105
## 47.57143 7.090076 10.60105
## 47.71429 7.090076 10.60105
## 47.85714 7.090076 10.60105
## 48.00000 7.090076 10.60105
## 48.14286 7.090076 10.60105
## 48.28571 7.090076 10.60105
## 48.42857 7.090076 10.60105
##
##
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 14.07615 14.47577 13.95045 14.77284 14.81448 16.32132 14.72386 14.89890
## [9] 14.97853 14.97853 14.97853 14.97853 14.97853 14.97853
##
## $岡山県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 10.77362 9.025370
## 46.71429 10.95937 9.097906
## 46.85714 10.35063 8.445002
## 47.00000 11.09149 9.142699
## 47.14286 11.05336 9.062342
## 47.28571 12.48210 10.449736
## 47.42857 10.80808 8.735194
## 47.57143 10.78905 8.613427
## 47.71429 10.76141 8.529007
## 47.85714 10.67019 8.389489
## 48.00000 10.58086 8.252866
## 48.14286 10.49330 8.118962
## 48.28571 10.40742 7.987623
## 48.42857 10.32313 7.858707
##
## $岡山県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 17.37868 19.12693
## 46.71429 17.99217 19.85364
## 46.85714 17.55027 19.45590
## 47.00000 18.45419 20.40299
## 47.14286 18.57559 20.56661
## 47.28571 20.16055 22.19291
## 47.42857 18.63963 20.71252
## 47.57143 19.00875 21.18437
## 47.71429 19.19565 21.42806
## 47.85714 19.28688 21.56757
## 48.00000 19.37621 21.70420
## 48.14286 19.46376 21.83810
## 48.28571 19.54964 21.96944
## 48.42857 19.63394 22.09836
##
##
## $広島県
## $広島県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 17.32142 17.43019 19.95334 21.52481 17.71748 18.78777 18.55460 18.92614
## [9] 19.13362 18.43257 17.67538 19.47473 19.28076 20.73072
##
## $広島県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 13.53941 11.537327
## 46.71429 13.26963 11.067164
## 46.85714 15.57835 13.262369
## 47.00000 16.52884 13.884131
## 47.14286 12.16383 9.223907
## 47.28571 12.91569 9.807192
## 47.42857 12.35600 9.074655
## 47.57143 12.45149 9.024013
## 47.71429 12.36137 8.776358
## 47.85714 11.39422 7.668344
## 48.00000 10.39230 6.536875
## 48.14286 11.94466 7.958487
## 48.28571 11.50778 7.393014
## 48.42857 12.72753 8.490901
##
## $広島県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 21.10344 23.10551
## 46.71429 21.59075 23.79322
## 46.85714 24.32834 26.64432
## 47.00000 26.52078 29.16548
## 47.14286 23.27112 26.21104
## 47.28571 24.65985 27.76835
## 47.42857 24.75321 28.03455
## 47.57143 25.40079 28.82827
## 47.71429 25.90587 29.49089
## 47.85714 25.47092 29.19680
## 48.00000 24.95845 28.81388
## 48.14286 27.00479 30.99097
## 48.28571 27.05373 31.16850
## 48.42857 28.73391 32.97054
##
##
## $山口県
## $山口県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 6.167262 3.580345 4.346201 4.300418 5.326281 4.481294 5.846836 3.999834
## [9] 4.144890 2.983238 4.243711 3.182892 3.532071 3.623799
##
## $山口県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 3.52651792 2.1285923
## 46.71429 0.70928109 -0.8105684
## 46.85714 1.26983066 -0.3587016
## 47.00000 1.12919280 -0.5495523
## 47.14286 2.05464794 0.3227499
## 47.28571 1.16564890 -0.5895473
## 47.42857 2.47626615 0.6919945
## 47.57143 0.50498745 -1.3450727
## 47.71429 0.58005845 -1.3070491
## 47.85714 -0.61883321 -2.5256548
## 48.00000 0.60089479 -1.3274956
## 48.14286 -0.48121358 -2.4208738
## 48.28571 -0.15811765 -2.1115856
## 48.42857 -0.07985604 -2.0404526
##
## $山口県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 8.808006 10.205932
## 46.71429 6.451409 7.971259
## 46.85714 7.422572 9.051104
## 47.00000 7.471643 9.150388
## 47.14286 8.597914 10.329812
## 47.28571 7.796938 9.552134
## 47.42857 9.217405 11.001677
## 47.57143 7.494681 9.344741
## 47.71429 7.709721 9.596828
## 47.85714 6.585310 8.492132
## 48.00000 7.886527 9.814917
## 48.14286 6.846997 8.786657
## 48.28571 7.222260 9.175728
## 48.42857 7.327454 9.288051
##
##
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 0.5682760 0.5471222 0.2227441 0.2967704 0.4936279 0.3824313 0.2247699
## [8] 0.4185694 0.5631824 0.5991178 0.6190629 0.4728398 0.4991365 0.6063059
##
## $徳島県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -1.133545 -2.034435
## 46.71429 -1.237576 -2.182338
## 46.85714 -1.580246 -2.534691
## 47.00000 -1.524327 -2.488358
## 47.14286 -1.345399 -2.318922
## 47.28571 -1.474353 -2.457275
## 47.42857 -1.649602 -2.641835
## 47.57143 -1.547360 -2.588060
## 47.71429 -1.435898 -2.494148
## 47.85714 -1.423379 -2.494024
## 48.00000 -1.426582 -2.509480
## 48.14286 -1.595694 -2.690709
## 48.28571 -1.592035 -2.699035
## 48.42857 -1.507262 -2.626117
##
## $徳島県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 2.270097 3.170987
## 46.71429 2.331821 3.276583
## 46.85714 2.025734 2.980179
## 47.00000 2.117868 3.081899
## 47.14286 2.332655 3.306178
## 47.28571 2.239215 3.222137
## 47.42857 2.099142 3.091375
## 47.57143 2.384499 3.425199
## 47.71429 2.562263 3.620512
## 47.85714 2.621614 3.692259
## 48.00000 2.664707 3.747606
## 48.14286 2.541373 3.636389
## 48.28571 2.590308 3.697308
## 48.42857 2.719874 3.838729
##
##
## $香川県
## $香川県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.14889 1.76894 1.76894 1.76894 1.76894 1.76894 1.76894 1.76894 1.76894
## [10] 1.76894 1.76894 1.76894 1.76894 1.76894
##
## $香川県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 0.7011443 -0.06524583
## 46.71429 0.2621288 -0.53552872
## 46.85714 0.2532764 -0.54906727
## 47.00000 0.2444754 -0.56252721
## 47.14286 0.2357250 -0.57590988
## 47.28571 0.2270242 -0.58921661
## 47.42857 0.2183722 -0.60244867
## 47.57143 0.2097682 -0.61560731
## 47.71429 0.2012115 -0.62869373
## 47.85714 0.1927012 -0.64170911
## 48.00000 0.1842366 -0.65465459
## 48.14286 0.1758170 -0.66753130
## 48.28571 0.1674416 -0.68034030
## 48.42857 0.1591098 -0.69308267
##
## $香川県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 3.596635 4.363025
## 46.71429 3.275751 4.073408
## 46.85714 3.284603 4.086947
## 47.00000 3.293404 4.100407
## 47.14286 3.302155 4.113789
## 47.28571 3.310855 4.127096
## 47.42857 3.319507 4.140328
## 47.57143 3.328111 4.153487
## 47.71429 3.336668 4.166573
## 47.85714 3.345178 4.179589
## 48.00000 3.353643 4.192534
## 48.14286 3.362063 4.205411
## 48.28571 3.370438 4.218220
## 48.42857 3.378770 4.230962
##
##
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 9.686107 9.384092 10.421131 10.837766 10.252039 10.863023 11.392673
## [8] 11.098846 11.098846 11.098846 11.098846 11.098846 11.098846 11.098846
##
## $愛媛県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 7.386924 6.169810
## 46.71429 6.707043 5.289900
## 46.85714 7.413319 5.821079
## 47.00000 7.532123 5.782221
## 47.14286 6.673265 4.778776
## 47.28571 7.030534 5.001737
## 47.42857 7.322253 5.167503
## 47.57143 6.888341 4.659435
## 47.71429 6.721831 4.404780
## 47.85714 6.561428 4.159463
## 48.00000 6.406504 3.922528
## 48.14286 6.256534 3.693169
## 48.28571 6.111072 3.470703
## 48.42857 5.969733 3.254544
##
## $愛媛県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 11.98529 13.20240
## 46.71429 12.06114 13.47828
## 46.85714 13.42894 15.02118
## 47.00000 14.14341 15.89331
## 47.14286 13.83081 15.72530
## 47.28571 14.69551 16.72431
## 47.42857 15.46309 17.61784
## 47.57143 15.30935 17.53826
## 47.71429 15.47586 17.79291
## 47.85714 15.63626 18.03823
## 48.00000 15.79119 18.27516
## 48.14286 15.94116 18.50452
## 48.28571 16.08662 18.72699
## 48.42857 16.22796 18.94315
##
##
## $高知県
## $高知県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 0.3486407 0.3564475 0.3636537 0.3703054 0.3764453 0.3821129 0.3873443
## [8] 0.3921733 0.3966307 0.4007452 0.4045431 0.4080488 0.4112848 0.4142718
##
## $高知県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -1.323069 -2.208018
## 46.71429 -1.336394 -2.232529
## 46.85714 -1.346986 -2.252544
## 47.00000 -1.355355 -2.268865
## 47.14286 -1.361911 -2.282141
## 47.28571 -1.366988 -2.292906
## 47.42857 -1.370859 -2.301596
## 47.57143 -1.373750 -2.308573
## 47.71429 -1.375843 -2.314133
## 47.85714 -1.377290 -2.318526
## 48.00000 -1.378218 -2.321955
## 48.14286 -1.378729 -2.324592
## 48.28571 -1.378908 -2.326579
## 48.42857 -1.378826 -2.328034
##
## $高知県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 2.020350 2.905300
## 46.71429 2.049289 2.945424
## 46.85714 2.074294 2.979852
## 47.00000 2.095966 3.009475
## 47.14286 2.114802 3.035032
## 47.28571 2.131214 3.057132
## 47.42857 2.145548 3.076285
## 47.57143 2.158096 3.092919
## 47.71429 2.169104 3.107395
## 47.85714 2.178781 3.120016
## 48.00000 2.187304 3.131041
## 48.14286 2.194826 3.140689
## 48.28571 2.201478 3.149148
## 48.42857 2.207369 3.156578
##
##
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 42.49203 31.61312 32.63342 43.83332 56.41975 61.27060 57.92927 48.14429
## [9] 39.65862 39.59620 46.79548 56.23742 59.83164 57.50332
##
## $福岡県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 28.93859 21.763830
## 46.71429 15.53488 7.023573
## 46.85714 15.23786 6.029205
## 47.00000 25.77535 16.216042
## 47.14286 37.81777 27.970484
## 47.28571 41.84287 31.558458
## 47.42857 37.16891 26.179044
## 47.57143 24.71957 12.319278
## 47.71429 14.30145 0.878175
## 47.85714 12.97612 -1.115703
## 48.00000 19.39472 4.889636
## 48.14286 28.19143 13.344776
## 48.28571 30.97465 15.698678
## 48.42857 27.49532 11.610040
##
## $福岡県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 56.04547 63.22023
## 46.71429 47.69135 56.20266
## 46.85714 50.02898 59.23763
## 47.00000 61.89128 71.45059
## 47.14286 75.02172 84.86901
## 47.28571 80.69832 90.98274
## 47.42857 78.68963 89.67950
## 47.57143 71.56900 83.96930
## 47.71429 65.01579 78.43907
## 47.85714 66.21629 80.30811
## 48.00000 74.19625 88.70133
## 48.14286 84.28342 99.13007
## 48.28571 88.68863 103.96460
## 48.42857 87.51133 103.39660
##
##
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.775491 2.775491 2.775491 2.775491 2.775491 2.775491 2.775491 2.775491
## [9] 2.775491 2.775491 2.775491 2.775491 2.775491 2.775491
##
## $佐賀県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 0.70654333 -0.3886916
## 46.71429 0.63709755 -0.4948998
## 46.85714 0.56983721 -0.5977656
## 47.00000 0.50456811 -0.6975861
## 47.14286 0.44112323 -0.7946166
## 47.28571 0.37935766 -0.8890789
## 47.42857 0.31914471 -0.9811667
## 47.57143 0.26037287 -1.0710505
## 47.71429 0.20294337 -1.1588813
## 47.85714 0.14676823 -1.2447938
## 48.00000 0.09176867 -1.3289083
## 48.14286 0.03787385 -1.4113334
## 48.28571 -0.01498024 -1.4921667
## 48.42857 -0.06685167 -1.5714972
##
## $佐賀県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 4.844440 5.939674
## 46.71429 4.913885 6.045883
## 46.85714 4.981146 6.148748
## 47.00000 5.046415 6.248569
## 47.14286 5.109860 6.345600
## 47.28571 5.171625 6.440062
## 47.42857 5.231838 6.532150
## 47.57143 5.290610 6.622033
## 47.71429 5.348039 6.709864
## 47.85714 5.404215 6.795777
## 48.00000 5.459214 6.879891
## 48.14286 5.513109 6.962316
## 48.28571 5.565963 7.043150
## 48.42857 5.617835 7.122480
##
##
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 1.8003536 0.6309484 1.2807685 0.9938626 1.1379805 1.7595610 1.1122077
## [8] 1.1923117 1.0262702 1.1003715 1.0496039 1.0812765 1.4054274 0.8914364
##
## $長崎県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -0.3144409 -1.433945
## 46.71429 -1.7179450 -2.961374
## 46.85714 -1.5538137 -3.054351
## 47.00000 -2.1225537 -3.772285
## 47.14286 -2.2974476 -4.116053
## 47.28571 -1.9381796 -3.895645
## 47.42857 -2.8445626 -4.939150
## 47.57143 -3.1473510 -5.444630
## 47.71429 -3.5951517 -6.041585
## 47.85714 -3.8199741 -6.424648
## 48.00000 -4.1359943 -6.881084
## 48.14286 -4.3647369 -7.247682
## 48.28571 -4.2850955 -7.297476
## 48.42857 -5.0354999 -8.173030
##
## $長崎県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 3.915148 5.034653
## 46.71429 2.979842 4.223271
## 46.85714 4.115351 5.615888
## 47.00000 4.110279 5.760010
## 47.14286 4.573409 6.392014
## 47.28571 5.457302 7.414767
## 47.42857 5.068978 7.163566
## 47.57143 5.531974 7.829253
## 47.71429 5.647692 8.094125
## 47.85714 6.020717 8.625391
## 48.00000 6.235202 8.980292
## 48.14286 6.527290 9.410235
## 48.28571 7.095950 10.108331
## 48.42857 6.818373 9.955903
##
##
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 5.365767 7.578621 6.951813 6.951813 6.951813 6.951813 6.951813 6.951813
## [9] 6.951813 6.951813 6.951813 6.951813 6.951813 6.951813
##
## $熊本県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 1.00952873 -1.296524
## 46.71429 1.88615469 -1.127255
## 46.85714 1.12919853 -1.953108
## 47.00000 0.81336547 -2.436133
## 47.14286 0.51300589 -2.895493
## 47.28571 0.22604644 -3.334359
## 47.42857 -0.04916084 -3.755253
## 47.57143 -0.31395149 -4.160215
## 47.71429 -0.56942576 -4.550929
## 47.85714 -0.81650287 -4.928801
## 48.00000 -1.05596012 -5.295019
## 48.14286 -1.28846183 -5.650600
## 48.28571 -1.51458105 -5.996419
## 48.42857 -1.73481621 -6.333240
##
## $熊本県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 9.722004 12.02806
## 46.71429 13.271087 16.28450
## 46.85714 12.774428 15.85673
## 47.00000 13.090261 16.33976
## 47.14286 13.390621 16.79912
## 47.28571 13.677580 17.23799
## 47.42857 13.952787 17.65888
## 47.57143 14.217578 18.06384
## 47.71429 14.473052 18.45456
## 47.85714 14.720129 18.83243
## 48.00000 14.959587 19.19865
## 48.14286 15.192088 19.55423
## 48.28571 15.418208 19.90005
## 48.42857 15.638443 20.23687
##
##
## $大分県
## $大分県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 13.32601 13.99449 13.77139 13.17995 13.46548 13.27315 12.34456 12.77954
## [9] 12.77954 12.77954 12.77954 12.77954 12.77954 12.77954
##
## $大分県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 11.347289 10.299816
## 46.71429 11.718553 10.513747
## 46.85714 11.232806 9.888962
## 47.00000 10.403455 8.933667
## 47.14286 10.469907 8.884147
## 47.28571 10.073461 8.379650
## 47.42857 8.953025 7.157655
## 47.57143 9.274340 7.418802
## 47.71429 9.137593 7.209666
## 47.85714 9.005799 7.008103
## 48.00000 8.878454 6.813346
## 48.14286 8.755137 6.624749
## 48.28571 8.635488 6.441761
## 48.42857 8.519198 6.263911
##
## $大分県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 15.30473 16.35221
## 46.71429 16.27042 17.47522
## 46.85714 16.30997 17.65381
## 47.00000 15.95645 17.42623
## 47.14286 16.46105 18.04681
## 47.28571 16.47283 18.16664
## 47.42857 15.73610 17.53147
## 47.57143 16.28473 18.14027
## 47.71429 16.42148 18.34940
## 47.85714 16.55327 18.55097
## 48.00000 16.68062 18.74572
## 48.14286 16.80393 18.93432
## 48.28571 16.92358 19.11731
## 48.42857 17.03987 19.29516
##
##
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 12.39619 11.46153 11.46153 11.46153 11.46153 11.46153 11.46153 11.46153
## [9] 11.46153 11.46153 11.46153 11.46153 11.46153 11.46153
##
## $宮崎県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 9.384400 7.790056
## 46.71429 8.301204 6.628227
## 46.85714 7.957754 6.102966
## 47.00000 7.645087 5.624783
## 47.14286 7.356165 5.182915
## 47.28571 7.086280 4.770162
## 47.42857 6.832103 4.381431
## 47.57143 6.591172 4.012960
## 47.71429 6.361611 3.661876
## 47.85714 6.141947 3.325929
## 48.00000 5.931001 3.003315
## 48.14286 5.727811 2.692562
## 48.28571 5.531579 2.392451
## 48.42857 5.341636 2.101958
##
## $宮崎県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 15.40797 17.00232
## 46.71429 14.62186 16.29484
## 46.85714 14.96531 16.82010
## 47.00000 15.27798 17.29828
## 47.14286 15.56690 17.74015
## 47.28571 15.83678 18.15290
## 47.42857 16.09096 18.54163
## 47.57143 16.33189 18.91010
## 47.71429 16.56145 19.26119
## 47.85714 16.78112 19.59713
## 48.00000 16.99206 19.91975
## 48.14286 17.19525 20.23050
## 48.28571 17.39149 20.53061
## 48.42857 17.58143 20.82111
##
##
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 2.528075 3.318678 3.465287 4.148764 4.148764 4.148764 4.148764 4.148764
## [9] 4.148764 4.148764 4.148764 4.148764 4.148764 4.148764
##
## $鹿児島県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 -1.5705552 -3.740239
## 46.71429 -1.4939485 -4.041599
## 46.85714 -1.5678245 -4.232193
## 47.00000 -0.9712116 -3.681563
## 47.14286 -0.9887146 -3.708332
## 47.28571 -1.0061583 -3.735009
## 47.42857 -1.0235431 -3.761597
## 47.57143 -1.0408696 -3.788096
## 47.71429 -1.0581385 -3.814506
## 47.85714 -1.0753503 -3.840829
## 48.00000 -1.0925056 -3.867066
## 48.14286 -1.1096049 -3.893217
## 48.28571 -1.1266488 -3.919284
## 48.42857 -1.1436378 -3.945266
##
## $鹿児島県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 6.626706 8.796389
## 46.71429 8.131305 10.678956
## 46.85714 8.498398 11.162766
## 47.00000 9.268740 11.979091
## 47.14286 9.286243 12.005860
## 47.28571 9.303686 12.032538
## 47.42857 9.321071 12.059125
## 47.57143 9.338398 12.085624
## 47.71429 9.355667 12.112034
## 47.85714 9.372879 12.138358
## 48.00000 9.390034 12.164594
## 48.14286 9.407133 12.190746
## 48.28571 9.424177 12.216812
## 48.42857 9.441166 12.242794
##
##
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## [1] 53.50747 50.30676 50.67333 52.71683 64.79226 56.22561 66.94046 61.22192
## [9] 61.22192 61.22192 61.22192 61.22192 61.22192 61.22192
##
## $沖縄県$lower
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 40.93493 34.27943
## 46.71429 36.66323 29.44078
## 46.85714 36.03696 28.28894
## 47.00000 37.15083 28.91069
## 47.14286 48.34909 39.64461
## 47.28571 38.94976 29.80448
## 47.42857 48.87026 39.30447
## 47.57143 41.07463 30.40929
## 47.71429 39.95142 28.69150
## 47.85714 38.88462 27.05997
## 48.00000 37.86650 25.50289
## 48.14286 36.89095 24.01091
## 48.28571 35.95303 22.57648
## 48.42857 35.04870 21.19343
##
## $沖縄県$upper
## Time Series:
## Start = c(46, 5)
## End = c(48, 4)
## Frequency = 7
## 80% 95%
## 46.57143 66.08000 72.73550
## 46.71429 63.95029 71.17274
## 46.85714 65.30969 73.05771
## 47.00000 68.28283 76.52298
## 47.14286 81.23542 89.93991
## 47.28571 73.50146 82.64674
## 47.42857 85.01066 94.57644
## 47.57143 81.36922 92.03455
## 47.71429 82.49243 93.75235
## 47.85714 83.55922 95.38388
## 48.00000 84.57734 96.94096
## 48.14286 85.55290 98.43294
## 48.28571 86.49082 99.86736
## 48.42857 87.39515 101.25042